About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. We're seeking an exceptional Research Scientist to join our Life Sciences team at Anthropic. Our team is building a world-class research group focused on making Claude a superhuman life sciences research assistant. This role sits at the intersection of machine learning, software engineering, and biology — you'll directly improve model capabilities on scientific tasks through post-training, evaluation design, and RL environment development. As a core member of our Life Sciences team, you'll work in a high-impact team that translates deep biological domain knowledge into model training objectives, benchmarks, and agentic workflows. You'll help establish Anthropic as a leader in AI-accelerated biology while shaping how frontier models reason about and execute computational biology tasks. This role offers a unique opportunity to shape how frontier AI models learn to do biology. You'll work alongside some of the world's best AI researchers while tackling problems that matter for human health and scientific understanding. If you're excited about turning your computational biology expertise into model capabilities, we want to hear from you. Key Responsibilities - Build and ship agentic tools and integrations that let Claude execute real life science workflows — bioinformatics pipelines, database queries, analysis notebooks, literature review - Design and build evaluation benchmarks that measure model capabilities on biology tasks — figure interpretation, bioinformatics, protocol reasoning, literature synthesis - Work closely with product and design teams to scope, prototype, and ship features for life sciences users - Partner with external biotech, pharma, and academic users to understand their workflows and turn feedback into product improvements - Build and maintain the engineering infrastructure behind our biology product surface — tool scaffolding, data pipelines, eval harnesses - Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement Minimum Qualifications - Experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar - Experience working in drug discovery or development at a biotech or pharma company, or conducted fundamental research in an academic setting — with an understanding of what real scientific workflows look like and where they break down - Strong software engineering skills: comfortable building production-quality Python, working in large codebases, and owning infrastructure end-to-end - Hands-on experience training or fine-tuning ML models (LLMs, protein language models, or other deep learning architectures) - A track record of shipping computational tools or pipelines that biologists actually use - Comfortable navigating ambiguity and defining problems in a rapidly evolving research environme

ABOUT THE TEAM OpenAI’s AI Success Engineer team partners with the world’s most ambitious government & partner organizations to translate cutting edge AI into real business and mission impact for governments of all levels from Local, State, Federal, and International. We guide customers and users journey from the first time they try ChatGPT Enterprise, automate a workflow, develop and execute a new skill, and create their first agent to scaled enterprise adoption of ChatGPT, Codex, our API and other novel capabilities. Our work spans technical integration and enablement, workflow transformation, inspiring and upskilling AI literacy and confidence across the workforce, sustained program, product and new capability delivery. Most importantly, we help each member of our customer's workforce, their teams, programs and missions meet their total potential. Our government customers have vital missions, and we must meet them with game-changing technology. Every engagement is an opportunity to shape how AI changes work, productivity, and innovation. This role sits at the center of that mission. ABOUT THE ROLE Governments work at a scale that is truly exponential on missions that are of critical importance to people, communities and nations. The AI Success Engineer role is the primary post-sales relationship for OpenAI’s most important customers. You are responsible for the end-to-end account management of critical Government and Partner customers. You will be helping Government Leaders/Partners appropriately and effectively use AI for their mission, while simultaneously investing in ensuring their people are AI-enabled and ready to advance positive outcomes that their constituents depend on them for. You will drive: the impact of our tools on their mission, account health and adoption, ensuring technical readiness, creating and executing on the deployment strategy, enabling, educating and training their workforce, identifying new use cases and upsell opportunities, and delivering measurable value to our customers with OpenAI’s ambitiously growing capabilities. This role blends technical depth, program and account management, customer advisory, training and enablement and product influence. You will partner deeply with customer teams, map workflows, lead configuration, oversee deployment plans, and guide customers toward high impact use cases that showcase the ways OpenAI tools can make a difference to the mission.. You drive our customers’ success and journey in an AI age. You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer use and value from our tools, guiding strategic use cases that get to production, and helping customers demonstrate tangible business and mission impact. You will bring key product feedback and insights to our product teams to ensure our capabilities continue to advance our customers' mission. IN THIS ROLE, YOU WILL: - Lead the relationship for post-sale customers and act as their trusted advisor on technical deployment, adoption, and value realization, this includes setting up, configuring and running API instances of our products. - Own customer success: account strategy & health; breadth, depth, velocity of adoption that drives mission impact, enablement and education; and ongoing technical deployment and success across your portfolio. - Be an expert in all of OpenAI products across our API and agentic platform, Codex, ChatGPT Enterprise, and more and conduct technical enablement and configuration sessions across them. - Train, educate and enable ChatGPT users to drive adoption and value. - Create and show customers how to make custom GPT’s, Skills, Agents, Plugins, Connectors, Codex and use all of the features and capabilities of our tools. - Design and lead hands-on activities like workshops, hackathons, and training sessions acr

About the Team The Personal AGI team is responsible for training and improving pre-trained models to be deployed into ChatGPT, the API, and potential future products. The team partners closely with research and product teams across the company, and conducts research as a final step to prepare for real world deployment to millions of users, ensuring that our models are safe, efficient, and reliable. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Own and pursue a research agenda to improve model capability and performance. - Collaborate closely with the other research and product teams, allowing customers to optimize their own models. - Build robust evaluations for tracking modeling improvements. - Design, implement, test, and debug code across our research stack. You might thrive in this role if you: - Have a deep understanding of machine learning and machine learning applications. - Have a working knowledge of relevant models, and building evaluations for model capability improvement. - Are comfortable diving into a large ML codebase to debug. - Thrive in a dynamic and technically complex environment. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disa

ABOUT THE TEAM Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security https://openai.com/index/updating-our-preparedness-framework/ that could scale to an extreme level of severity. Our work involves: 1. Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. 2. Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. 3. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework https://openai.com/index/updating-our-preparedness-framework/, and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. ABOUT THE ROLE This role leads the Automated Red Teaming (ART) effort: building scalable, research-driven systems that continuously uncover failure modes in our models and safeguards, and translate those findings into actionable, production-facing improvements. The goal is to reduce expected harm by finding the highest-leverage, least-covered weaknesses early and reliably. IN THIS ROLE, YOU'LL: - Own the research and technical direction for automated red teaming across catastrophic risk areas, with an initial emphasis on: - Automated classifier jailbreak discovery (cyber and bio). - Automated bio threat-development elicitation (worst-feasible planning uplift). - CoT monitoring evasion probing (and adjacent loss-of-control evaluations). - Partner closely with: - Vertical risk teams (Cyber, Bio, Loss of Control) to define threat models, prioritize targets, and land mitigations. - The Classifiers team to turn discovered attacks into training data, evals, and measurable robustness gains. - Product / Engineering / Safety stakeholders to ensure ART outputs are operationally useful. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Feel a strong pull toward AI safety, and you’re motivated by reducing real-world catastrophic risk (not just publishing cool results). - Love breaking systems (responsibly) — you get energy from finding weird, high-severity failure modes and turning them into concrete fixes. - Have strong applied research instincts, especially around evaluations: you’re good at designing experiments that are reproducible, interpretable, and hard to fool. - Bring hands-on experience with LLMs and agents, including multi-turn behaviors, tool use, and the ways models adapt to constraints. - Are comfortable building scalable automation, not just prototypes — you can turn red-teaming ideas into pipelines that run continuously and produce high-signal outputs. - Have solid software engineering fundamentals (data structures, algorithms, testing discipline) and you can work effectively in a production-adjacent environment. - Think in threat models and incentives, and you naturally ask “what would an attacker do next?” or “how would this fail under pressure?” - Can translate messy findings into action, communicating clearly with researchers, engineers, product, and policy — and driving alignment on what to fix first. - Care about efficiency and prioritization, and you’re happy to say “no” to low-leverage work to focus on what moves the risk needle. - Nice to have: - Experience in adversarial ML, security research / red teaming, abuse prevention systems, or large-scale eval infrastructure. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportun

Scale plays a vital role in the development of AI applications. Our customer base is growing exponentially, and you will be on the front lines, ensuring that the world's most innovative companies become passionate, lifelong Scale customers. Solutions Engineers partner closely with AEs, Product, and MLEs to lead prospective customers through pre-sales, delivering customized demos and pilots to secure the “technical win”. Solutions Engineers scope customer technical requirements and develop an actionable SOW. They will work closely with the delivery team to help with initial implementation. Solutions Engineers are relentlessly curious about customer needs and pain points. They employ their expert Scale product knowledge and GenAI knowledge to design solutions that best address these needs. Solutions Engineers are strong relationship builders, great project managers, and provide technical expertise. You will: - Partner with Scale AEs on the customer journey, delivering tailored demos and prototypes according to the customer's requirements. - Develop technical domain expertise in Generative AI / large language model applications for Enterprise use cases, including customers in financial services, insurance, SaaS, and similar enterprises. - Be accountable for securing the “technical win” by unblocking technical challenges - Interact with customers daily to understand their needs and design solutions to better serve them. - Design and develop “Scopes of Work” by breaking down customer challenges into a project plan - Work closely with forward-deployed Software and Machine learning Engineers to develop agents in the initial post-sales stage - Work with AEs and PMs to identify customer-specific feature requests. - Drive strategic initiatives to improve the efficiency and effectiveness of the Solution Engineering team. Ideally, you'd have: - Strong engineering background with prior experience working with clients in a pre or post-sales capacity to realize business goals. - Prior experience developing with Python, Java and/or other web development languages. - Experience working in enterprise SaaS, cloud tech, finance, fintech or similar industries in a technical capacity with end-customer engagement. - A track record as a self-starter, motivated to independently unblock technical issues in the field with the customer, away from the mothership. - Presentation skills with a high degree of technical credibility when speaking with executives and front-line engineers. - High level of comfort communicating effectively across internal and external organizations. - Intellectual curiosity, empathy, and ability to operate with high velocity. Nice to haves: - GenAI Experience - Forward deployed engineering experience - Machine Learning Experience Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equ

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We're seeking an exceptional Research Scientist to join the team. This role combines deep computational biology expertise with frontier AI capabilities, positioning Anthropic at the forefront of AI-driven scientific discovery. As one of the first computational members of this Life Sciences research group, you'll work on a high-impact team that operates at the intersection of computational and experimental biology. You'll bring broad computational biology experience to bear across the team's projects, driving discoveries from large-scale computational analysis of biological data through to results our experimental scientists can test, and moving flexibly between problems as the science demands. You'll have substantial access to Claude and you'll help establish how computational biology operates at Anthropic. This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for scientific understanding and biomedicine. If you're excited about using your computational expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you. Key responsibilities - Build, run, and maintain the analysis pipelines that back the team's experimental programs: sequence analysis at petabyte scale, structural bioinformatics, phylogenetic and comparative genomics, design and analysis of high-throughput functional screens, biological sequence modeling, etc. - Partner directly with experimental biologists to design experiments that produce high-quality data, and turn results around fast enough to immediately inform the next experiment - Draw on the literature and curated biological knowledge bases alongside primary data to generate and prioritize hypotheses for experimental follow-up - Stand up and maintain the team's computational infrastructure: data ingestion, workflow orchestration, internal databases, and the interfaces that make all of it accessible to both researchers and AI agents <li class="font-claude-response-body whitespace-normal b

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs. More resources to learn about our work: - Our research blog - covering advances including Monosemantic Features and Circuits - An Introduction to Interpretability from our research lead, Chris Olah - The Urgency of Interpretability from CEO Dario Amodei - Engineering Challenges Scaling Interpretability - directly relevant to this role - 60 Minutes segment - Around 8:07, see a demo of tooling our team built - New Yorker article - what it's like to work on one of AI's hardest open problems Even if you haven’t worked on interpretability before, the infrastructure expertise is similar to what's needed across the lifecycle of a production language model: - Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips - Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model's internal activations mid-forward-pass - for example, adding a "steering vector" - Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission The science keeps scaling - and it's now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI. Responsibilities: - Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector a

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. Responsibilities - Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on - Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts - Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks - Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse You may be a good fit if you - Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry - Have proficiency in Python and experience building ML systems - Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems - Are worried about misuse risks of AI systems, and want to work to mitigate them - Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders Strong candidates may also have experience with - Language modeling and transformers - Building classifiers, anomaly detection systems, or behavioral ML - Adversarial machine learning or red-teaming - Interpretability or probes - Reinforcement learning - High-performance, large-scale ML systems The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 - $500,000 USD Logistics Minimum education: Bac

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: We are looking for Research Engineers to help us redesign how Claude interacts with external data sources. Many of the paradigms for how data and knowledge bases are organized assume human consumers and constraints. This is no longer true in a world of LLMs! Your job will be to design new architectures for how information is organized, and train language models to optimally use those architectures. Responsibilities: - Designing and implementing from scratch new information architecture strategies - Performing finetuning and reinforcement learning to teach language models how to interact with new information architectures - Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data - Designing and evaluating advanced agentic search capabilities. You may be a good fit if you: - Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using - Have good machine learning research experience - Have experience developing software that utilizes Large Language Models such as Claude - Are results-oriented, with a bias towards flexibility and impact - Pick up slack, even if it goes outside your job description - Enjoy pair programming (we love to pair!) - Want to partner with world-class ML researchers to develop new LLM capabilities - Care about the societal impacts of your work - Have clear written and verbal communication Strong candidates will also have experience with: - Collaborating with product teams to quickly prototype and deliver innovative solutions - Building complex agentic systems that utilize LLMs - Developing scalable distributed information retrieval systems, such as search engines, knowledge graphs, RAG, indexing, ranking, query understanding, and distributed data processing The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of expe

This isn't a live vacancy - it's an invitation. Framestore's London studio is actively building relationships with artists who are working at the frontier of AI-assisted visual development, and we want to hear from you before the right role appears. We're the studio behind some of the most ambitious visual storytelling in cinema - from the creatures of Guardians of the Galaxy, How To Train Your Dragon, and Paddington to the worlds of Harry Potter, Project Hail Mary , and Wicked . Now we're building the next chapter, using AI not as a shortcut, but as a creative amplifier. Our proprietary tools - including Futon, our generative AI pipeline integration - are giving artists new ways to shape, iterate, and realise complex visuals at a pace and scale that wasn't possible before. We're looking for artists who feel at home in this space. Whether you're a concept artist who has built custom ComfyUI workflows, a matte painter who uses AI to explore compositional ideas, or someone whose practice has evolved in directions that don't fit the standard job description - we want to see what you're making. What we’re looking for - An AI-native creative approach: You use generative tools as a core part of your practice - not as a finishing step, but as part of how you think. You've built workflows, iterated on models, or found ways to generate imagery that genuinely pushes the work forward. - Strong visual foundations: A command of frame, composition, light, and colour that doesn't depend on the tool. Your AI-assisted work still reads as intentional, directed, and refined - because the craft underneath it is solid. - Curiosity about production pipelines: You're interested in how concept work informs downstream VFX - how a visual development image transitions into a 3D pipeline, how a lighting decision in concept shapes what gets rendered months later. You don't need to have worked in a major VFX studio, but you're interested in how it all connects. - A portfolio that shows your thinking: We care less about where you've worked and more about what you're making. Show us work that demonstrates how you solve visual problems. Process work, personal projects, and experimental output are all welcome. Tools we work with - Futon: Framestore's own ML & generative AI integration layer - built into our VFX pipeline. - ComfyUI: Node-based generative workflows. Experience here is a strong signal. - Nuke: Compositing environment used to build living, production-ready concept imagery. - Blender: Open-source 3D widely used for blocking, lighting, and generative AI integration via custom nodes. - Unreal / Realtime: Real-time render engines increasingly part of visual development workflows. - Gaussian Splatting: Frontier technique - experience or genuine interest is a plus. Send your portfolio and a short note about how you work to recruiters@framestore.com with the subject line: Visual Development — AI Talent Pool . There's no fixed deadline. We review applications on an ongoing basis and will reach out when a relevant opportunity arises. We review all applications manually and do not use artificial intelligence to screen candidates. We welcome applications from artists at all career stages. If you need any adjustmen

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic is building Treasury with scale and automation in mind from the start. We're creating modern, well-controlled processes that can grow with the business - supported by strong systems, clear governance, and practical use of AI-enabled tools. We're designing AI-native workflows from day one, with human in the loop, SOX-grade controls built in rather than bolted on. This role reports to the Treasury Ops Lead and owns the company's global cash and liquidity function. You'll run the daily cash desk - positioning, forecasting, funding, and payments, while building the visibility and liquidity structures (pooling, concentration, repatriation) that let a multi-entity, multi-currency company see and deploy its cash as one balance sheet. This is a hands-on role for someone who enjoys both execution and process building. You should be comfortable running the daily position while designing the cash architecture the company will run on at 10x scale. Key responsibilities - Own daily cash positioning across all entities and currencies, track balances, consolidate activity, and set the daily funding plan - Direct cash management operations: wire/ACH execution, cash concentration, sweep structures, and cash pooling activities - Build and run the short- and medium-term cash forecast (13-week and beyond), including variance tracking, scenario modeling, and reporting to leadership - Identify and implement strategies to optimize working cash balances and minimize idle funds - right cash, right entity, right currency, right time - Monitor liquidity risk, counterparty exposure, and concentration limits; escalate before they become issues - Build a single global view of cash - consolidate balance and transaction reporting across all banks, entities, and currencies into one source of truth - Partner with the Accounting team to design and manage intercompany funding and settlement processes, including cross-border movement, netting, and documentation - Streamline cross-border transaction flows and optimize cash repatriation strategies in partnership with Accounting, Tax, and Legal - Support pooling / in-house-bank structures as the entity footprint grows - Manage bank relationships from the services side including fee analysis, service reviews, wallet allocation, and connectivity - to get the most out of our banking partners - Design the "Claudification" layer for cash: identify which workflows to be automated, build the automation, keep humans in the decisions that need judgment - Support TMS buy vs build evaluation/implementation with a cash-and-liquidity lens: bank connectivity (SWIFT, APIs, host-to-host), balance/transaction reporting, cash-position and forecast modules - Partner with Finance Systems to simplify and automate cash reporting and forecasting - Own cash and liquidity data quality - the source of truth that positioning, forecasting, and investment decisions depend on - Execute cash processes with strong focus on controls, documentation, segregation of duties, and audit readiness - Support development and maintenance of

ABOUT THE TEAM Integrity Data Science sits at the center of OpenAI’s mission to deploy powerful AI responsibly. We help ensure people can trust our products by building measurement systems, experimentation practices, and detection/mitigation strategies that protect OpenAI and our users from misuse, fraud, and evolving adversarial behaviors. As the scope and urgency of Integrity work expands across product surfaces and go-to-market motion, we’re hiring a dedicated Data Science Manager to scale the team, strengthen execution across multiple Integrity domains, and deepen partnership with Product, Engineering, Operations, and adjacent orgs (e.g., Growth, Ads). This role is based in our San Francisco HQ (in-office). ABOUT THE ROLE As Data Science Manager, Integrity, you will lead a team of data scientists working across trust & safety, fraud prevention, risk analysis, measurement, and modeling. You’ll be accountable for building a high-performing DS function that can keep pace with fast-moving threats—and for shaping the analytical strategy that informs how OpenAI detects, measures, and mitigates integrity risks at scale. This is a highly cross-functional leadership role. You’ll help set the roadmap with Integrity Product/Engineering leaders, evolve team structure and operating rhythms, raise the bar on technical rigor (experimentation, causal inference, modeling, metrics), and develop a culture of proactive, high-leverage impact. Many of the challenges in this space are emergent—new misuse patterns appear as the technology and ecosystem evolves—so this role requires strong judgment, comfort with ambiguity, and an ability to build systems that scale. IN THIS ROLE, YOU WILL: - Lead and scale a high-impact Integrity Data Science team—hiring, coaching, and developing DS ICs (and potentially future managers) while setting a strong technical and cultural bar. - Drive strategy across multiple Integrity domains (policy enforcement, bot detection, fraud prevention, IP theft, risk measurement, abuse prevention), balancing near-term response with durable systems. - Build and institutionalize analytical rigor: clear metric frameworks, experimentation standards, monitoring/alerting, and repeatable evaluation approaches for Integrity interventions. - Partner deeply with Product & Engineering to shape roadmaps, prioritize the right bets, and translate ambiguous risk signals into practical product and platform decisions. - Evolve team structure and operating model as the org scales—defining ownership boundaries, improving processes, and creating leverage through better tooling and AI-assisted workflows. - Enable cross-org outcomes, supporting partners outside Integrity (e.g., Growth, Ads, GTM) where integrity risks intersect with product and business goals. - Communicate clearly with senior leadership, synthesizing complex tradeoffs, surfacing risk, and driving alignment on priorities and success metrics. - Push the team toward an AI-leveraged operating mode, using modern tooling and model capabilities to accelerate detection, triage, analysis, and iteration. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have deep experience leading and scaling Data Science teams, ideally in trust & safety, fraud/abuse, security, risk, or other adversarial problem spaces in fast-moving environments. - Bring strong technical grounding across modern DS techniques (experimentation, causal inference, anomaly detection, risk modeling, measurement design) and can coach others to execute with rigor. - Have a track record of building durable partnerships across DS, Engineering, Product, and Operations—able to influence without authority and create shared accountability. - Are excellent at hiring, mentoring, and developing technical talent, and can build a culture that is both high-bar and supportive. - Can translate messy, evolving threats into clear frameworks, metrics, and decisions—and keep the team focused on the highest-leverage work. - Are comfortable operating in ambigu

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the RL Teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators. You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will: - Invent, design and implement RL environments and evaluations. - Conduct experiments and shape our research roadmap. - Deliver your work into training runs. - Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic. You may be a good fit if you: - Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch). - Have worked across the stack – kernels, model code, distributed systems. - Know how to balance research exploration with engineering implementation. - Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: - Experience with reinforcement learning. - Experience porting ML workloads between different types of accelerators. - Familiarity with LLM training methodologies. The annual compensation range for this role is listed below. For sales roles, the range provided is th

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation. Representative projects: - Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows. - Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models. - Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows. - Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research. You may be a good fit if you: - Are proficient in Python and async/concurrent programming with frameworks like Trio - Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX) - Have industry experience in machine learning research - Can balance research exploration with engineering implementation&lt

About Pinecone Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. Pinecone's mission is to make AI knowledgeable. More than 5000 customers across various industries have shipped AI applications faster and more confidently with Pinecone's developer-friendly technology. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. About the Team and Role: As a Business Development Representative, you’ll be the primary catalyst for user activation and expansion. You will work at the critical intersection of our Product-Led Growth (PLG) motion and our high-growth revenue engine. Your core mission is to identify high-potential users within our self-serve funnel and proactively convert them into qualified sales opportunities. You are part researcher and part automation hacker. You will partner closely with our Revenue Operations team to combine creative outreach with GTM (Go-To-Market) engineering workflows (using tools like Clay, n8n, Zapier, etc.) to deliver personalized, data-backed touchpoints at scale. You'll collaborate closely with Sales, Customer Success, and Solutions Engineering to accelerate our PLG motion, turning a robust sign ups & usage funnel into a predictable revenue engine and setting a new standard for AI-native GTM. Responsibilities: - Drive Sales Opportunities: Proactively engage users exhibiting high-potential usage signals (e.g., specific API patterns, advanced feature experimentation, new workspace invites) to guide them toward opportunities for the sales team. - Identify & Convert: Execute highly personalized, data-driven outreach sequences to connect with AI practitioners, technical leads, and business decision-makers. Your goal is to understand their platform behaviors and signals in order to generate qualified sales leads. - Surface PQLs: Partner with RevOps and Growth to analyze product telemetry and surface Product-Qualified Leads (PQLs) with clear expansion potential (e.g., users building RAG, Agents, semantic search, or recommender systems). - Work on GTM Automation: Collaborate with RevOps to build and maintain scalable sales automation workflows using tools like Clay, n8n, Zapier, Retool, and our own product's query insights to dramatically reduce manual prospecting. - Innovate on Engagement: Go beyond standard outbound. Use AI-assisted personalization, shareable technical sandbox links, LinkedIn video DMs, and voice notes to provide tangible value in every interaction. - Create Feedback Loops: Work cross-functionally with Product and Marketing to provide direct feedback on inbound quality, user friction points, and emerging persona trends from the front lines. Requirements: - Has 1–2+ years of BDR/SDR experience in a fast-paced, technical startup—ideally in developer tools, data infrastructure, or the AI/ML space. - Deeply understands modern PLG + Sales hybrid models and is energized by the challenge of bridging product usage to revenue. - Is comfortable and credible when speaking to technical personas (e.g., Founders, ML Engineers, Data Scientists) and can translate complex product value into tangible business outcomes. - Possesses a "guide" or "teacher" mindset, genuinely motivated by helping users solve problems and achieve "aha" moments that lead to deeper product adoption. - Thrives in ambiguous and fast-changing environments; you have high agency, are proactive, and are guided by data. - Is passionate about AI, automation, and GTM innovation. You naturally use tools like ChatGPT, Clay, Gong, and Loom to be more effective. - Loves metrics (PQLs, conversion rates, consumption) but doesn’t wait to be told what to do, constantly iterating on playbooks and documenting what works. Bonus Points: - Direct experience with vector databases, LLM applications, embeddings, or developer ecosystems. - You've built your own prospecting automations or played with tools lik

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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow. Responsibilities: - Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability - Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure - Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance - Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams - Build and maintain production logging, monitoring dashboards, and evaluation infrastructure - Add new capabilities to the training codebase, such as long context support or novel architectures - Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams - Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned You May Be a Good Fit If You: - Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems - Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other - Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure - Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs - Excel at debugging complex, ambiguous problems across multiple layers of the stack - Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents - Are passionate about the work itself and want to refine your craft as a research engineer - Care about the societal impacts of AI and responsible scaling Strong Candidates May Also Have: - Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale - Contributed to open-source LLM frame

We believe AI will fundamentally change how games are built and played, from content creation to player experience, and we’re building at the frontier of that shift. Puzzle Cats builds casual mobile games played by tens of millions of people worldwide. We’re a small, high-performing team that moves faster than most studios think is possible, taking games from concept to App Store quickly and continuously iterating on live games with millions of active players. We’re looking for an AI Product Engineer who wants to build something big and move fast. This is a hands-on, highly product-oriented role for someone who wants to invent, ship, and scale AI-native game experiences. You’ll work across the full development process, from prototype to production, collaborating with designers, engineers, and product teams to launch new games and improve live ones. Features you build can reach millions of players within weeks. We’re especially interested in engineers who don’t just tolerate AI tools, they live inside them. Someone who naturally uses tools like Claude, Cursor, OpenAI, or coding agents as part of how they build, understands the code they generate, and knows when to prompt a solution versus writing it from scratch. You’ll own features end-to-end, from gameplay systems and backend infrastructure to analytics, building, shipping, measuring, and iterating quickly based on real player data. We ship fast, test aggressively, and aren’t afraid to kill ideas — including AI ones — if the data doesn’t support them. What You’ll Do - Use AI to create new player experiences that wouldn’t be possible otherwise - Build and ship mobile game features in Unity (C#) for iOS and Android, including gameplay mechanics, onboarding flows, monetization experiments, and live-ops systems - Prototype and launch new game ideas quickly, moving from concept to playable builds in weeks, not months - Design and build AI-powered gameplay and product features, including content generation, personalization, and AI-driven mechanics - Design and implement AI systems and pipelines, including RAG, embeddings, tool use, and agents - Optimize AI systems for cost, latency, and scalability, including token usage, caching, and model selection - Develop and maintain backend systems for live games, including leaderboards, events, A/B testing infrastructure, and cloud services (GCP/Firebase) - Collaborate closely with designers and analysts to interpret player data and turn insights into gameplay improvements - Own the full lifecycle of your features, including implementation, analytics instrumentation, debugging, and performance optimization - Review code and maintain high engineering standards, including AI-generated code Must-Haves - Strong problem-solving ability, a hacker mindset, and deep object-oriented programming knowledge - Ability to write clean, maintainable code and move quickly without sacrificing quality - Experience integrating real AI features into products, not just using AI tools, but shipping or meaningfully prototyping model-driven functionality - Experience integrating LLMs into production systems using OpenAI, Anthropic, or open-source models - Strong intuition for how LLMs behave, including prompt design, failure modes, and output quality - Experience building and iterating on AI features using real user data - 3+ years of production software engineering experience, ideally with shipped mobile apps or games built in Unity (C#) for iOS and Android - Experience with Python or JavaScript, cloud infrastructure/back-end systems, and Git - Ability to analyze player data and metrics to improve gameplay and product decisions - Strong critic

About the Team The Integrity team at OpenAI is dedicated to ensuring that our cutting-edge technology is not only revolutionary, but also secure from a myriad of adversarial threats. We strive to maintain the integrity of our platforms as they scale. The Integrity team is at the front lines of defending against misuse in all its forms: content abuse, scaled attacks, and other actions that could undermine the user experience or harm our operational stability. About the Role As a Machine Learning Engineer in OpenAI's Integrity team, you will have the opportunity to work with some of the brightest minds in AI. You’ll work on state-of-the-art models and classifiers, experiment with new architecture and approaches, and push forward our abilities in content and user understanding. You’ll help turn research breakthroughs into tangible solutions that improve the trust and safety of our platform. If you're excited about training LLMs and building ML models, this role is your chance to make a significant mark. In this role, you will: - Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact. - Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives. - Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches. - Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices. - Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large. You might thrive in this role if you: - Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field. - Demonstrated experience in deep learning and transformers models - Experience with content understanding or abuse prevention with LLMs is a plus - Proficiency in frameworks like PyTorch or Tensorflow - Strong foundation in data structures, algorithms, and software engineering principles. - Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization - Excellent problem-solving and analytical skills, with a proactive approach to challenges. - Ability to work collaboratively with cross-functional teams. - Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines - Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employm

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Safeguards team builds the systems that detect and mitigate misuse of our AI models, from individual policy violations to sophisticated, coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals, which let us identify harmful behavior cheaply and at scale. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change. Running machine learning workloads at our scale often requires solving novel systems problems. You'll identify those problems and build the abstractions, pipelines, and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it. Key responsibilities - Build and scale the infrastructure and data pipelines behind Safeguards machine learning research - Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result - Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath - Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve - Take the highest-value research workflows from experiments to reliable, production-grade jobs - Improve the throughput, cost, and reliability of large-scale inference and scoring workloads - Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time Minimum qualifications - Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python - Experience building and operating data-intensive or distributed systems in production - Experience building tooling or infrastructure that other engineers or researchers use as a dependency - Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems - Ability to debug performance and correctness problems across an unfamiliar stack - Strong written and verbal communication skills, and a collaborative approach to technical decisions Preferred qualifications - Experience with high-performance, large-scale machine learning systems - Familiarity with language modeling and transformers, including working with model internals - Experience

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Note: we don't have open Research Manager positions on the Interpretability team at this time. However, we're actively growing our team of Research Engineers and Research Scientists . If you're excited about interpretability research and open to an individual contributor role, we encourage you to apply. About the Interpretability team When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team’s mission is to reverse engineer how trained models work, and Interpretability research is one of Anthropic’s core research bets on AI safety. We believe that a mechanistic understanding is the most robust way to make advanced systems safe. People mean many different things by "interpretability". We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do "biology" or "neuroscience" of neural networks, or as treating neural networks as binary computer programs we're trying to "reverse engineer". We aim to create a solid scientific foundation for mechanistically understanding neural networks and making them safe (see our vision post ). We have focused on resolving the issue of "superposition" (see Toy Models of Superposition , Superposition, Memorization, and Double Descent , and our May 2023 update ), which causes the computational units of the models, like neurons and attention heads, to be individually uninterpretable, and on finding ways to decompose models into more interpretable components. Our subsequent work which found millions of features in Claude 3.0 Sonnet, one of our production language models, represents progress in this direction. In our most recent work , we developed methods that allow us to build circuits using features and use these circuits to understand the mechanisms associated with a model's computation and study specific examples of multi-hop reasoning, planning, and chain-of-thought faithfulness on Claude Haiku 3.5, one of our production models.” This is a stepping stone towards our overall goal of mechanistically understanding neural networks. A few places to learn more about our work and team are this introduction to Interpretability from our research lead, Chris Olah, Stanford CS25 lecture given by Josh Batson, and TWIML AI podcast with E

ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. ABOUT THE ROLE ElevenReader is our fastest growing app, and creative is one of our biggest growth levers. We’re looking for a Creative Producer to build the design and marketing systems, experiments, and creative engine that powers performance marketing across every major acquisition channel. This role sits at the intersection of creative design, AI, and performance marketing. You’ll create be our lead creative strategist building scalable creative workflows using generative AI, developing testing frameworks, launching experiments across Meta, Google, YouTube, Reddit, and emerging platforms, and building the creative systems that enable rapid iteration. You’ll partner closely with Performance Marketing, Design, and Growth Analytics to continuously discover what creative drives acquisition at scale. This is one part strategist and one part builder role for someone who loves combining exceptional creative taste with rigorous experimentation. WHAT YOU WILL DO - Building the creative systems, templates, mood boards, and AI workflows that power high-velocity ad production - Developing visually-stunning static imagery and videos for new ad campaigns using generative media platforms, including ElevenCreative - Creating and launching creative testing programs across Meta, Google, YouTube, Reddit, and emerging acquisition channels - Developing a structured experimentati

About the company: Located in Redondo Beach, CA, GungHo Online Entertainment America Inc. is the U.S. subsidiary of the Japanese game company GungHo Online Entertainment, Inc. Publishing titles across multiple platforms ranging from console to handheld and mobile, our group is comprised of veterans from various aspects of the industry brought together by one simple thing: a love of gaming. Position Summary: This position is responsible for designing, finetuning, and shipping machine learning and generative AI features for our new, internally developed game, as well as general debugging and troubleshooting. You will work at the intersection of applied ML and game development. What you will be doing: - Design, implement, and debug ML/AI-driven features for our internally developed game, with a focus on LLM-powered NPC dialogue and model infrastructure. - Finetune, evaluate, and optimize models for in-game use, balancing quality against latency, memory, and cost. - Build and maintain inference systems and data pipelines that integrate cleanly with the game engine and runtime. - Contribute to gameplay features and general game code alongside your ML work. This is a hands-on role on both sides. - Ship other generative AI features as the game evolves. - Discuss, design, and plan AI features with artists, game designers, and gameplay engineers. - Test prototypes of future titles and AI systems to provide constructive feedback. - Attend industry conventions and live events as needed. Qualifications: - 4 years experience in programing, professionally or in school is a plus. - B.S. or equivalent in Computer Science is preferred. - C++ and game-engine experience (Unreal) is a huge plus - Demonstrated hands-on experience finetuning or deploying ML models. Whether through professional work, personal projects, or self-directed experimentation. We're looking for hands-on ML experience outside of coursework. - Solid general programming proficiency (any language); comfortable working in a production codebase. - No specific stack required — we care more about what you have actually built than which tools you used. - Experience deploying models for low-latency, real-time inference is a plus. - Familiarity with 3D graphics, shaders, or GPU programming is a plus. - Strong communication skills. - Desire to learn and to grow, including into the game-development side. - Ability to work in small, agile team environments. - Passion for narrative games Why you want to work here: At GungHo, we’re enthusiastic about creating fun games and we’re always looking for talented individuals with whom we can share our passion. We have a highly collaborative culture where everyone wants to work together to get things done! Perks: - 100% employer-paid insurances: Health Insurance (PPO Medical, Dental, and Vision), Life Insurance, Accidental Death and Dismemberment insurance, and Long Term Disability Insurance - 401(k) with employer match - Competitive paid time off: Vacation, Personal time, Sick Leave, Holidays, and Winter Breaks. - An incentive may be provided based on business performance. Business Hours:  10:00 AM - 7:00 PM in Pacific Time Salary Range:  $75,000 - $119,000 per year (DOE) <span style="fon

About the Team OpenAI’s Compute organization turns ambitious AI research into real-world capability by delivering the compute infrastructure behind our most advanced models. The team works across software, hardware, facilities, operations, and engineering disciplines to make enormous amounts of compute available, reliable, and efficient. As the demand for frontier AI grows, so does the complexity of the systems required to support it. Scaling this infrastructure means solving problems that cut across distributed systems, ML infrastructure, GPU fleets, power, cooling, networking, manufacturing, supply chain, and data center delivery. Our work is focused on expanding the compute foundation that enables OpenAI to train more capable models, including systems like GPT-5.6, and make frontier AI available to more people, products, and workflows. We’re looking for exceptional people across many disciplines to help build the next generation of AI infrastructure at a scale few organizations have attempted. About the Role We are hiring across a broad range of roles to help design, build, scale, and operate OpenAI’s compute infrastructure. Depending on your background, you may work on large-scale distributed systems, ML infrastructure, hardware systems, manufacturing, supply chain, data center development, or the physical engineering systems required to bring massive compute capacity online. You’ll work with teams across research, engineering, hardware, operations, and infrastructure to solve high-impact problems at extraordinary scale. This may include improving system reliability, accelerating deployment timelines, increasing operational efficiency, designing new infrastructure, or helping bring new compute platforms and facilities from concept to production. This is an opportunity to work on one of the most important infrastructure challenges in AI: building the compute foundation required to train and serve increasingly capable frontier models. Key Responsibilities - Help build, scale, and operate OpenAI’s global compute infrastructure. - Solve complex problems across software, hardware, manufacturing supply chain, and data center systems. - Improve the reliability, performance, efficiency, and scalability of critical infrastructure. - Partner with cross-functional teams to bring new compute capacity online quickly and reliably. - Identify bottlenecks across technical, operational, and physical systems, and develop practical solutions. - Build tools, processes, systems, or infrastructure that improve execution at scale. - Contribute to the long-term architecture and operational maturity of OpenAI’s compute footprint. Qualifications - Have experience building, scaling, or operating complex technical systems. - Enjoy working on ambiguous, high-impact problems where the path forward is not always defined. - Are comfortable collaborating across disciplines, including software, hardware, operations, and physical infrastructure. - Have strong technical judgment and a bias toward execution. - Care deeply about reliability, speed, safety, and operational excellence. - Are excited by the challenge of building infrastructure at unprecedented scale. - Want your work to directly support the development and deployment of frontier AI. Preferred Skills - Have experience with AI infrastructure, high-performance computing, distributed systems, GPU clusters, or cloud-scale platforms. - Have worked on hardware systems, manufacturing, supply chain, data center development, or large capital infrastructure projects. - Have domain expertise in civil, controls, mechanical, hardware, electrical, thermal, power, networking, or facilities engineering. - Have helped bring new technical platforms, data centers, factories, or large-scale systems from concept to production. - Have experience operating in fast-moving environments where technical depth and execution speed both matter. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring

About the Team The Personal AGI team is responsible for training and improving pre-trained models to be deployed into ChatGPT, the API, and potential future products. In the Model Experience team, we shape the default character and behavior of ChatGPT: how the model communicates, responds to users, uses its capabilities, and behaves across different contexts and languages. Our goal is to make every interaction with ChatGPT thoughtful, helpful, and trustworthy. We take an opinionated view of what good human–AI interaction should look like, then turn that vision into real model behavior through human data, evaluations, reward models, and post-training. Our work sits at the intersection of research, product, and model design. We partner closely with teams across OpenAI to conduct research and ensure our models are thoughtful, safe, reliable to serve millions of users. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research and the quality of human-AI interaction. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Own and pursue a research agenda to improve model capability and performance. - Collaborate closely with the other research and product teams, allowing customers to optimize their own models. - Build robust evaluations for tracking modeling improvements. - Design, implement, test, and debug code across our research stack. You might thrive in this role if you: - Have a deep understanding of machine learning and machine learning applications. - Have good judgment about model behavior and can communicate this judgment effectively. - Enjoy taking ambitious, qualitative problems and turning them into concrete training interventions. - Have a working knowledge of relevant models, and building evaluations for model capability improvement. - Are comfortable diving into a large ML codebase to debug. - Thrive in a dynamic, technically complex, and collaborative environment. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand. As Technical Recruiter, Infrastructure, you'll join the small team of recruiters who hire for that organization, owning full lifecycle recruiting for your searches and partnering with infrastructure leaders to turn ambiguous needs into clear search strategies. Key responsibilities - Own full lifecycle recruiting for a portfolio of roles across the Infrastructure organization, from intake through offer and close - Run structured intakes with infrastructure hiring managers, translating ambiguous needs into scoped requirements, calibrated bars, and search strategies - Build and maintain pipelines of specialized infrastructure talent, with an emphasis on passive candidates - Refine infrastructure interview loops, take-home assignments, and scorecards alongside hiring managers, your recruiting counterparts, and Recruiting Operations - Develop deep domain knowledge aligned with the teams you support, so you can identify niche talent with the right specific domain fit - Advise hiring managers with market data and candid calibration feedback, and influence decisions through credibility rather than volume - Partner with Compensation, People Partners, and Mobility to structure equitable offers and guide candidates to close - Handle sensitive role and candidate information with discretion, including for searches whose scope is confidential Minimum qualifications - Deep full lifecycle recruiting experience, with substantial time supporting infrastructure, platform, or comparably technical engineering organizations - Ability to hold a substantive technical conversation about infrastructure domains such as Kubernetes and container orchestration, cloud networking, cluster networking, and systems languages, and to evaluate technical qualifications rather than match keywords - Proficiency with an applicant tracking system like Greenhouse and other modern sourcing tools - Experience partnering directly with hiring managers on intake, bar calibration, and interview loop design - Sound independent judgment on candidate quality, and the ability to independently partner with multiple hiring managers on complex searches - A strong sense of ownership over your work, and the adaptability to adjust as priorities and hiring needs shift - Genuine interest in Anthropic's mission and in the role a strong infrastructure function plays in achieving it <h2&gt

PLAY, GROW and WIN To be a part of Virtuos means to be a creator. At Virtuos, we harness the latest technologies to make games better and more immersive than ever before. That is why we pride ourselves in constantly pushing the boundaries of possibility since our founding in 2004. Virtuosi is a team of experts – people who have come together to share their mutual passion for making and playing games. People with the same enthusiasm for exploring new ideas and the constant drive to excel in their field. People who believe in earning success through dedication. At Virtuos, we are at the forefront of gaming, creating exciting new experiences daily. Join us to Play, Grow and Win – together. ABOUT THE POSITION About Us: Black Shamrock is a leading game development studio that thrives on creating captivating worlds and immersive gameplay. We are pioneering the future of game development with a AAA project that integrates cutting-edge AI systems and realism into gameplay. As part of our talented team, you’ll have the opportunity to shape an animation pipeline at the bleeding edge of to shape an animation pipeline at the bleeding edge of real-time performance, procedural behavior, and AI-generated motion working on an ambitious project that pushes boundaries in both creativity and technology. About the Position: We’re seeking a Senior or Lead Technical Animator to bridge the gap between traditional animation and advanced AI-driven systems. This role is pivotal in architecting runtime animation behaviors, collaborating with AI teams, and leading the animation integration process across gameplay and systems. <div class="OutlineElement Ltr SCXW183694806 BCX0" style="-webkit-tap-highlight-color: transparent; -webkit-text-stroke-width: 0px; -webkit-user-drag: none; background-color: rgb(255, 255, 255); clear: both; color: rgb(0, 0, 0); cursor: tex

We are seeking a highly technical Product Security Engineer to join our Public Sector Infrastructure & Security team. Our Product Security Engineers ensure the security and integrity of our products and services. You will conduct in-depth code reviews, implement security best practices, and influence the overall security strategy. Your expertise in TypeScript, Python, Kubernetes, CI/CD, SAST, DAST, and terraform orchestration will be crucial in identifying and mitigating potential security vulnerabilities. You will also structure complex problems, diagnose root causes independently, and clearly explain the mechanics and significance of security vulnerabilities, including their exploitability and potential impact. You will: - Conduct in-depth code reviews to identify and remediate security vulnerabilities. - Evaluate and enhance the security of our product offerings, through RFC and service review. - Implement and maintain CI/CD pipelines with a strong focus on security. - Perform Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) to identify vulnerabilities in production code. - Utilize terraform orchestration to ensure secure and efficient infrastructure management. - Guide engineering teams to build robust long-term solutions that consider security and privacy. - Clearly explain the mechanics and significance of security vulnerabilities, including their exploitability and potential impact. - Influence the security strategy and direction of the team, advocating for best practices and continuous improvement. Ideally, you’d have: - at least a Secret level government security clearance - Proven experience as a Security Engineer with a focus on product security. - Proficiency in NodeJS, TypeScript, Python, and/or Kubernetes. - Strong understanding of modern Javascript application design. - Production experience with Kubernetes backed services - Hands-on experience with SAST and DAST tools and methodologies. - Familiarity with terraform orchestration for infrastructure management. - You can structure complex problems and diagnose root causes independently, providing actionable insights without requiring manager input. - Excellent communication skills, with the ability to clearly present technical concepts and their implications to both technical and non-technical stakeholders. - Demonstrated ability to influence security strategies and drive improvements within a team. - Relevant security certifications (e.g., CISSP, CEH, OSCP) are a plus. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision cover

About the Team The Privacy Team at OpenAI is committed to building a secure and trustworthy platform. Our area of responsibility encompasses all OpenAI products and systems that process user data. We provide cross-functional partners with the tools needed to ensure that all products adhere to the highest standards of data privacy and legal compliance. Our approach to prioritizing responsible data use is integral to OpenAI's mission of safely introducing Artificial General Intelligence (AGI) that offers widespread benefits. About the Role We’re in search of a Software Engineer with experience building data pipelines and working closely with members of the Legal team. This role is perfect for someone who's passionate about the intersection of systems, privacy, and legal compliance. You will architect, design, and write backend systems responsible for handling some of the most sensitive data at OpenAI. This role is based in Dublin, Ireland. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design, build, and maintain back-end systems and services that power privacy and data compliance functions within our API products and consumer applications. - Work closely with legal advisors and other engineers to respond to court orders and other legal processes, all while upholding strict data privacy and legal standards. - Identify opportunities for automation and build the tools that enable other teams to automate tasks involving customer data. - Develop and implement data handling policies and procedures in compliance with legal and ethical standards, ensuring the integrity and confidentiality of user data. You might thrive in this role if you: - Have experience building data pipelines, especially for legal processes and investigative workflows. - Can translate legal requirements into technical solutions and explain technical solutions to a non-technical audience. - Take responsibility for problems from beginning to end, and are prepared to acquire any missing knowledge necessary to get the job done. - Create tools to speed up your own and your colleagues’ workflows, particularly when pre-existing solutions are inadequate. - Deeply care about user experience and take pride in developing products that meet customer needs while ensuring privacy. - Have a background in security investigations or experience working in collaboration with trust and safety, legal, and engineering teams. Compensation, Benefits and Perks This is a position with OpenAI Ireland Ltd., which controls the hiring and management of this position. Total compensation includes an annual salary, generous equity, and benefits. - Medical, dental, and vision insurance for you and your family - Mental health and wellness support - PRSA plan with 6% employer matching - Unlimited time off - Annual learning & development stipend (€1,400 per year) About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants w

About the Team Our Safety Systems https://openai.com/safety/safety-systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. Key focus areas include: catastrophic risk, mental health, teen safety and multimodal safety. About the Role Providing access to frontier AI systems raises complex questions around dual-use science and catastrophic risk. How should models respond to requests involving chemical synthesis, biological experimentation, or pathogen research? Where is the boundary between legitimate scientific inquiry and information that could enable misuse? How do we design policies that meaningfully reduce risk without unnecessarily restricting beneficial research? This is a senior role in which you’ll help shape policy creation and development at OpenAI for addressing biological and chemical risks. You will develop structured policy frameworks and taxonomies to guide safe model behavior. This role sits at the intersection of biosecurity expertise, AI safety research, and policy design. You will help ensure that frontier AI systems can support beneficial life sciences research, such as drug discovery, public health, and biosafety, while reducing the risk that these capabilities could be misused. Our relevant publications: - Preparedness framework https://openai.com/index/updating-our-preparedness-framework/ - Preparing for future AI capabilities in biology https://openai.com/index/preparing-for-future-ai-capabilities-in-biology/ - Safety evaluations hub https://openai.com/safety/evaluations-hub/ - OpenAI GPT5 System Card https://openai.com/index/gpt-5-system-card/ - Evaluating Fairness in ChatGPT https://openai.com/index/evaluating-fairness-in-chatgpt/ - Improving Model Safety Behavior with Rule-Based Rewards https://openai.com/index/improving-model-safety-behavior-with-rule-based-rewards/ - OpenAI Model Spec https://openai.com/index/introducing-the-model-spec/ Your Responsibilities: - Design and maintain model policies governing chemical and biological risk, defining how models should safely handle dual-use scenarios. - Develop structured taxonomies of chemical and biological risk that inform model training data, evaluation benchmarks, and safety monitoring systems. - Translate biosecurity and chemical security expertise into actionable model behavior, working closely with research and engineering teams to operationalize policy in training and evaluation pipelines. - Develop a broad range of subject matter expertise while maintaining agility across topics. - Identify emerging risk vectors where frontier AI capabilities could meaningfully lower barriers to harmful activity and develop mitigation strategies. - Engage with internal and external subject-matter experts in biosecurity, biodefense, and chemical safety to ensure policies reflect real-world risk landscapes. You might thrive in this role if you: - Have strong domain expertise in chemistry, biology, biosecurity, or related fields and are motivated to translate that expertise into principled, operational policies that scale to frontier AI systems. - Have experience researching or working with LLMs, machine learning, AI governance, technology policy, or related areas, and enjoy tackling structured reasoning and classification problems—such as defining boundaries between legitimate scientific inquiry and potentially harmful applications. - Have experience designing, refining, or enforcing policies or safeguards for complex systems, whether in AI/ML environments, scientific research governance, national security contexts, or other high-stakes technical domains. - Are comfortable navigating a

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow. Responsibilities: - Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability - Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure - Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance - Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams - Build and maintain production logging, monitoring dashboards, and evaluation infrastructure - Add new capabilities to the training codebase, such as long context support or novel architectures - Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams - Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned You May Be a Good Fit If You: - Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems - Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other - Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure - Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs - Excel at debugging complex, ambiguous problems across multiple layers of the stack - Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents - Are passionate about the work itself and want to refine your craft as a research engineer - Care about the societal impacts of AI and responsible scaling Strong Candidates May Also Have: - Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale - Contributed to open-source LLM frame

ABOUT THE TEAM The Alignment Science team at OpenAI studies the science of intent alignment: how to train models to understand what users are actually asking for, act faithfully on that intent while respecting safety constraints, verify what they did, and report their limitations honestly. Our work sits alongside broader value alignment efforts, but this team focuses on scalable methods for ensuring instruction-following, honesty, and robustness as models become more capable. We work on both sides of alignment research: producing externally publishable results and integrating promising techniques into the models OpenAI deploys. Recent team research on model confessions studies how models can be trained to honestly report shortcomings after their original answer, including failures involving hallucination, instruction following, scheming, and reward hacking. That work reflects a broader agenda: build scalable and general methods to ensure models follow human intent. The team uses a mix of training and evaluation methods, with a focus on reinforcement learning. We care about rigorous, quantitative research that can translate into safer model behavior. ABOUT THE ROLE As a Research Engineer / Research Scientist on the Alignment team, you will design and run experiments that help increasingly capable models follow user intent, remain calibrated about correctness and risk, and honestly surface their own mistakes. You will work on hands-on model training, evaluation design, and research infrastructure, while helping turn promising alignment methods into techniques that can be used in frontier model development. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. We are also open to exceptional remote candidates who can operate independently and collaborate closely with the team. IN THIS ROLE, YOU WILL: - Design and implement alignment experiments focused on intent following, honesty, calibration, and robustness. - Train and evaluate models using reinforcement learning, and other empirical ML methods. - Develop evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming. - Study methods that encourage models to verify their behavior and report shortcomings honestly, including confession-style training objectives. - Build monitoring and inference-time interventions that ensure compliant behavior or surface model issues to users or downstream systems. - Investigate how alignment methods scale with model capability, compute, data, context length, action length, and adversarial pressure. - Integrate successful techniques into model training and deployment workflows. - Produce externally publishable research when results advance the broader science of alignment. - Collaborate with researchers and engineers across post-training, RL, evaluations, safety, and product-facing teams. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs. - Have excellent engineering skills in Python and modern ML frameworks such as PyTorch. - Bring mathematical rigor, quantitative taste, and comfort turning ambiguous research questions into measurable experiments. - Have experience with reinforcement learning, post-training, preference optimization, scalable oversight, model evaluation, or adjacent empirical ML research. - Can operate with high independence and do not need close day-to-day handholding. - Enjoy fast-paced, collaborative research environments where priorities shift as models and evidence change. - Have a strong record in technical problem solving, such as competitive programming, math contests, systems work, or similarly rigorous engineering and research projects. - Care about building AI systems that are trustworthy, honest, and reliable in high-stakes settings. - Are motivated by making concrete

About the Team The Privacy Engineering team builds secure, reliable systems that help OpenAI meet its legal obligations while protecting user data. We partner closely with Legal and Engineering teams across OpenAI to support lawful data access requests and other critical legal workflows. Our work turns complex, high-stakes processes into auditable and dependable technical systems with clear human oversight and strong privacy and security controls. About the Role We’re looking for a full-stack Software Engineer to build the internal tools and data pipelines that power lawful data access request workflows and Legal Operations. You will work across product and data systems to make authorized retrieval and case handling accurate, efficient, and auditable. This role is well suited to someone who enjoys translating ambiguous operational requirements into durable systems, cares deeply about sensitive-data handling, and wants to improve both technical reliability and the day-to-day experience of the people operating these workflows. This role is based in San Francisco, CA, with two additional locations under consideration: London, UK, and Dublin, Ireland. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design, build, and operate backend systems and workflow tooling for the full lifecycle of lawful data access requests, from intake and scoping through authorized retrieval, review, preparation, and audit. - Build reliable data pipelines and interfaces across products and data stores so authorized teams can locate and handle the right records accurately and reproducibly. - Implement least-privilege access, approval gates, provenance, audit trails, data minimization, and safe failure modes for sensitive workflows. - Partner with Legal and Legal Operations to translate legal and operational requirements into clear technical designs and intuitive operator experiences. - Identify responsible automation opportunities that reduce repetitive work while preserving human review, judgment, and accountability. - Own production systems through testing, observability, incident response, documentation, and continuous reliability improvements. - Help define the architecture and roadmap for reusable privacy and legal-infrastructure foundations as OpenAI’s products and obligations evolve. You might thrive in this role if you: - Have experience building or operating systems for lawful data access requests and understand the domain’s legal-process lifecycle, privacy and security constraints, auditability requirements, and operational sensitivities. - Strong backend engineering fundamentals and experience building production services or data-intensive systems. - Ability to reason carefully about correctness, authorization, security, and privacy when working with sensitive data. - A track record of turning ambiguous requirements into pragmatic plans and communicating technical tradeoffs clearly. - End-to-end ownership, including operating what you build, and comfort learning unfamiliar products and domains. - Care for operator experience and a drive to make complex workflows safer, clearer, and more efficient. - Experience collaborating across Engineering, Legal, operations, Privacy, and Security stakeholders. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, ag

About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role As an Engineer on our hardware optimization and co-design team, you will co-design future hardware from different vendors for programmability and performance. You will work with our kernel, compiler and machine learning engineers to understand their unique needs related to ML techniques, algorithms, numerical approximations, programming expressivity, and compiler optimizations. You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity! This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. Key Responsibilities - Co-design future hardware for programmability and performance with our hardware vendors - Assist hardware vendors in developing optimal kernels and add support for it in our compiler - Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features - Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front end networking - Work with machine learning engineers, kernel engineers and compiler developers to understand their vision and needs from high performance accelerators - Manage communication and coordination with internal and external partners - Influence the roadmap of hardware partners to optimize them for OpenAI’s workloads. - Evaluate potential partners’ accelerators and platforms. - As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings. Qualifications - 4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware. - Strong experience in software/hardware co-design - Deep understanding of GPU and/or other AI accelerators - Experience with CUDA, Triton or a related accelerator programming language - Experience driving Machine Learning accuracy with low precision formats - Experience with system performance modeling and analysis to optimize ML model deployment - Strong coding skills in C/C++ and Python - Are familiar with the fundamentals of deep learning computing and chip architecture/microarchitecture. - Able to actively collaborate with ML engineers, kernel writers, compiler developers, system engineers, chip architects/microarchitects Preferred Skills - PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing. Compilers or other Systems - Strong understanding of LLMs and challenges related to their training and inference About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities: - Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development - Independently lead small research projects while collaborating with team members on larger initiatives - Design, run, and analyze scientific experiments to advance our understanding of large language models - Optimize and scale our training infrastructure to improve efficiency and reliability - Develop and improve dev tooling to enhance team productivity - Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications: - Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field - Strong software engineering skills with a proven track record of building complex systems - Expertise in Python and experience with deep learning frameworks (PyTorch preferred) - Familiarity with large-scale machine learning, particularly in the context of language models - Ability to balance research goals with practical engineering constraints - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment - Care about the societal impacts of your work Preferred Experience: - Work on high-performance, large-scale ML systems - Familiarity with GPUs, Kubernetes, and OS internals - Experience with language modeling using transformer architectures - Knowledge of reinforcement learning techniques - Background in large-scale ETL processes You'll thrive in this role if you: - Have significant software engineering experience - Are results-oriented with a bias towards flexibility and impact - Willingly take on tasks outside your job description to support the team - Enjoy pair programming and collaborative work - Are eager to learn more about machine learning research - Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects - Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research - View research and engineering as

ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. ABOUT THE ROLE ElevenLabs is building the creative suite for the AI generation. Millions of creators and marketers already use our platform to generate speech, music, images, and video, and our social presence needs to match the speed and ambition of the product. We are looking for a Social Media Manager to own and elevate the social presence across both the ElevenLabs and ElevenCreative brands. You will own ElevenCreative on X and LinkedIn, ElevenLabs on Instagram, TikTok, and YouTube, and our community on Discord. This is a hands-on role at the intersection of content strategy, lightweight video editing, and community. You will plan the calendar, capture product moments with our AI creative producers and product teams, and do the recording and editing yourself using tools like Screen Studio, assembling clips, adding text and motion, and adapting each post natively for its platform. You will stay ahead of trends and cultural moments, up-level the brand voice and the quality bar across channels, track performance, and use the data to keep improving what we publish. The right person lives on social platforms, understands what makes content work natively on each one, and has strong opinions about how an AI-native brand should show up. You should be comfortable writing sharp copy, running a content calendar across multiple channels, and thinking strategically abou

About the Team The RL and Reasoning team drives the core reasoning paradigm and has created groundbreaking innovations such as o1 and o3. They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale. About the Role As a Research Engineer/Research Scientist at OpenAI, you will advance the frontier of AI alignment and capabilities through cutting-edge RL methods. Your work will sit at the heart of training intelligent, aligned, and general-purpose agents, including the systems that power various models. We’re looking for people who have a background in reinforcement learning research, are able to iterate quickly, and are proficient at coding. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if: - You love being on the cutting edge of RL and language model research. - You’re a self-starter who takes initiative and ownership of ideas, driving them to completion. - You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time. - You thrive in a fast-paced, dynamic, and technically complex environment where rapid iteration is key. - You’re comfortable diving into a large ML codebase to debug and improve it. - You have a deep understanding of machine learning and machine learning applications. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241. OpenAI Global

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team We are seeking passionate Research Scientists and Engineers to join our growing Pre-training team in Zurich. We are involved in developing the next generation of large language models. The team primarily focuses on multimodal capabilities: giving LLMs the ability to understand and interact with modalities other than text. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Responsibilities In this role you will interact with many parts of the engineering and research stacks. - Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development - Independently lead small research projects while collaborating with team members on larger initiatives - Design, run, and analyze scientific experiments to advance our understanding of large language models - Optimize and scale our training infrastructure to improve efficiency and reliability - Develop and improve dev tooling to enhance team productivity - Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications & Experience We encourage you to apply even if you do not believe you meet every single criterion. Because we focus on so many areas, the team is looking for both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer spectrum to apply. - Degree (BA required, MS or PhD preferred) in Computer Science, Machine Learning, or a related field - Strong software engineering skills with a proven track record of building complex systems - Expertise in Python and deep learning frameworks - Have worked on high-performance, large-scale ML systems, particularly in the context of language modeling - Familiarity with ML Accelerators, Kubernetes, and large-scale data processing - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment You'll thrive in this role if you - Have significant software engineering experience - Are able to balance research goals with practical engineering constraints - Are happy to take on tasks outside your job description to support the team - Enjoy pair programming and collaborative work - Are eager to learn more about machine learning research &l

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: - Build, profile and optimize our training and inference framework - Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation - Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: - Strong excitement about system optimization - Experience with multi-node LLM training and inference - Experience with developing large-scale distributed ML systems - Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. - Strong written and verbal communication skills and the ability to operate in a cross functional team environment Nice to haves: - Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $189,600 - $237,000 USD PLEASE NOTE: Our po

ABOUT THE TEAM OpenAI Finance is responsible for ensuring the organization is set up for success in pursuit of its mission. Within Finance, the Procurement team serves as a trusted advisor to the business, optimizing investments, securing essential resources, and enabling OpenAI’s mission through efficient and effective procurement practices. The Extended Workforce team helps OpenAI access specialized and flexible talent while creating a consistent, compliant, and high-quality experience for hiring managers, workers, vendors, and internal partners. As part of an AI-native organization, we are also rethinking how extended workforce programs should operate, using AI, automation, data, and new ways of working to reduce friction, improve decision-making, and build more scalable workforce solutions. Our goal is not simply to modernize traditional contingent workforce practices, but to help define what best-in-class extended workforce management looks like in an AI-first company. ABOUT THE ROLE We’re looking for an experienced, hands-on Extended Workforce Program Manager to operate and continuously improve key elements of OpenAI’s extended workforce program. Reporting to the Head of Extended Workforce, you will be a trusted partner responsible for translating program priorities into clear plans, scalable solutions, and measurable outcomes. This is an individual contributor role with significant cross-functional ownership and influence. You will take ownership of complex workstreams, solve ambiguous operational challenges, and partner closely with our Extended Workforce Operations team, Global Compliance Lead, and cross-functional stakeholders to strengthen the systems, workflows, and partnerships that enable the program to scale globally. While operational excellence is at the heart of this role, we are equally excited about what comes next. We believe AI presents an opportunity to fundamentally rethink how extended workforce programs operate. We are looking for someone who enjoys challenging conventional thinking, experimenting with new ideas, and helping build a more intelligent, automated, and scalable operating model for the future. This role is based in San Francisco, CA. We use a hybrid work model of three days per week in the office and offer relocation assistance to new employees. IN THIS ROLE, YOU WILL: - Partner with the Head of Extended Workforce to set direction for assigned workstreams, then own their end-to-end execution by translating priorities into clear plans, milestones, and measurable outcomes. - Partner closely with the Extended Workforce Operations team and Global Compliance Lead to ensure program operations are efficient, scalable, and aligned with compliance requirements. - Advise hiring managers and business stakeholders on workforce solutions, exercising sound judgment across stakeholder experience, speed, cost, compliance, risk, and operational scalability. - Translate business needs, stakeholder feedback, and operational insights into improved workflows, automation, systems, reporting, and user experiences. - Identify, test, and implement practical applications of AI, automation, analytics, and decision-support tools that reduce manual work, improve decisions, and enable a more scalable operating model. - Identify root causes behind recurring operational challenges and build durable processes, systems, and frameworks that prevent them from recurring. - Manage strategic supplier relationships to drive optimal performance, strengthen service delivery, address gaps, and ensure partners continue to meet OpenAI’s evolving workforce needs. - Establish program health measures and operational insights that identify systemic issues, inform priorities, and drive measurable improvements in service, efficiency, adoption, supplier performance, and risk. - Lead the rollout and adoption of new processes and tools, coordinating testing, documentation, training, communications, and success measurement. - Independently res

As a ML Research Engineer on Embark's Machine Learning team, you will help shape the future of storytelling tools and systems for video games. We believe modern AI can unlock entirely new creative workflows for narrative design, quest building, lore creation, and player-facing reactivity. Our goal is to investigate how we can design new tools and systems that enable creators to be more ambitious, collaborate more effectively, and explore ideas with less friction rather than simply automating existing workflows. In this role, you will research, prototype, and build AI-driven narrative systems - exploring what's possible before optimizing for production. You'll work closely with writers, and designers to explore different levels of AI agency to intentionally design where AI agency makes sense and where it doesn't. This is a role for someone who is genuinely passionate about narrative and storytelling, has a research mindset, and is curious about what new AI techniques can enable in games. Example of responsibilities - Build gameplay systems and tools that enable new narrative workflows and faster iteration for creators - Collaborate with designers and narrative teams to understand creative needs and turn them into intuitive tooling - Research and prototype AI-driven narrative systems - quest generation, reactive dialogue, authoring tools, and beyond - Design and run lightweight experiments to evaluate creative AI applications - Work with writers and designers to explore and prototype AI-native gameplay mechanics that preserve authorial intent while enabling emergence and reactivity - Prototype experimental features quickly, then help mature the best ideas into robust solutions - Help design and evaluate different degrees of AI autonomy, balancing creative ambition, reliability, and control - Help define what 'good' looks like when AI is involved in storytelling We would love if you have - A research or engineering background with hands-on experience in ML or LLM applications - A genuine passion for narrative: games, film, interactive fiction, or otherwise - Curiosity and comfort working in ambiguous, exploratory problem spaces - A creator mindset: you enjoy building things that make other people more effective - Comfort collaborating with writers and designers and translating creative goals into technical solutions - Professional English communication skills Additionally, it's a bonus if you have - Experience working in Unreal Engine or with C++ - Experience with narrative systems, quest pipelines, or other creator-driven game features - A background in ML, NLP, generative models, or evaluation methodologies - Python experience, or interest in working across engine + services/tooling At Embark we offer competitive salaries, a generous profit-sharing program, and much more, but most of all we invite you to take part of a journey into the unknown, to build creative, surprising and beautiful experiences together. We welcome people from all backgrounds and are looking forward to reading more about you (in English)!

ABOUT THE TEAM Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security https://openai.com/index/updating-our-preparedness-framework/ that could scale to an extreme level of severity. Our work involves: 1. Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. 2. Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. 3. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework https://openai.com/index/updating-our-preparedness-framework/, and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. ABOUT THE ROLE Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. As we push toward AGI, cybersecurity becomes one of the most important and urgent frontiers: the same systems that can accelerate productivity can also accelerate exploitation. As a Researcher for cybersecurity risks, you will help design and implement an end-to-end mitigation stack to reduce severe cyber misuse across OpenAI’s products. This role requires strong technical depth and close cross-functional collaboration to ensure safeguards are enforceable, scalable, and effective. You’ll contribute directly to building protections that remain robust as products, model capabilities, and attacker behaviors evolve. IN THIS ROLE, YOU WILL: - Design and implement mitigation components for model-enabled cybersecurity misuse—spanning prevention, monitoring, detection, and enforcement—under the guidance of senior technical and risk leadership. - Integrate safeguards across product surfaces in partnership with product and engineering teams, helping ensure protections are consistent, low-latency, and scale with usage and new model capabilities. - Evaluate technical trade-offs within the cybersecurity risk domain (coverage, latency, model utility, and user privacy) and propose pragmatic, testable solutions. - Collaborate closely with risk and threat modeling partners to align mitigation design with anticipated attacker behaviors and high-impact misuse scenarios. - Execute rigorous testing and red-teaming workflows, helping stress-test the mitigation stack against evolving threats (e.g., novel exploits, tool-use chains, automated attack workflows) and across different product surfaces—then iterate based on findings. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have a passion for AI safety and are motivated to make cutting-edge AI models safer for real-world use. - Bring demonstrated experience in deep learning and transformer models. - Are proficient with frameworks such as PyTorch or TensorFlow. - Possess a strong foundation in data structures, algorithms, and software engineering principles. - Are familiar with methods for training and fine-tuning large language models, including distillation, supervised fine-tuning, and policy optimization. - Excel at working collaboratively with cross-functional teams across research, security, policy, product, and engineering. - Have significant experience designing and deploying technical safeguards for abuse prevention, detection, and enforcement at scale. - (Nice to have) Bring background knowledge in cybersecurity or adjacent fields. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectr

ABOUT THE TEAM The Codex Research team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE As a member of the Codex Research team, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. IN THIS ROLE, YOU MIGHT: - Design and run experiments that improve agentic model behavior across coding, tool use, function calling, computer use, multi-agent collaboration, long-horizon tasks, factuality, instruction following, and calibrated reasoning. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex, API/platform, ChatGPT, and general-agent product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals,

ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. ABOUT THE ROLE ElevenReader is our fastest growing app that brings content to life for consumers. As we scale globally, we need a Growth Marketer who can turn creative ideas and strategic partnerships into one of our most powerful acquisition channels. You’ll own the strategy and execution of growth campaigns that bring millions of new users to ElevenReader through partnerships, integrated marketing, and innovative activations. Think: loyalty programs, co-marketing campaigns, OOH activations, offer-based collaborations, retail partnerships, and testing entirely new distribution channels. This is a highly cross-functional role that sits at the intersection of growth marketing, creative, partnerships, and performance marketing. You’ll develop creative campaigns, launch new acquisition channels, build scalable partnership playbooks, and rigorously measure what works. You’ll work closely with Performance Marketing to ensure every activation drives measurable full-funnel user growth while strengthening the ElevenReader brand. WHAT YOU WILL DO - Building and scaling strategic consumer partnerships that drive acquisitions for ElevenReader - Developing integrated marketing campaigns spanning partnerships, paid media, email, social, OOH, PR, and product surfaces - Expanding existing telecom & loyalty partnerships while launching new opportunities with partnersIdentifyin

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We are seeking a Recruiting Research Scientist to join our People Data Solutions team. You’ll be the research expert supporting our Recruiting organization, using rigorous scientific methods to advance our understanding of recruiting funnels, interview effectiveness, candidate experience, and recruiting capacity. This role sits at the intersection of organizational science, behavioral research, and people strategy – developing novel frameworks and conducting systematic research that drives evidence-based people decisions across our growing organization. This role offers the opportunity to make a significant impact on both our recruiting practices and the broader field of people science at a leading AI safety company. Responsibilities Research design & scientific inquiry - Design and execute systematic research studies to answer fundamental questions about recruiting funnel health, assessment quality, candidate experience, and quality of hire - Generate and test hypotheses about sourcing strategies, interview design, and selection decisions using rigorous experimental and quasi-experimental methods - Conduct mixed-method research to understand what are the drivers and blockers to recruiting operations. - Navigate research ethics considerations when studying candidate data, ensuring responsible research practices Selection & assessment research - Design and execute validation studies to assess the quality of interviews and other selection tools - Utilize psychometric techniques to analyze and improve interviewer calibration and rating consistency - Lead investigative research into innovative approaches for candidate assessment Metrics design and governance - Design the metrics framework for recruiting org health — defining the canonical KPIs, dimensions, and definitions that leadership uses to understand funnel performance, capacity, and hiring quality - Establish the governance and definitional rigor that keeps metrics consistent across tools and reporting surfaces Analytical solution building - Architect analytical solutions that convert research insights into actionable products, empowering stakeholders to execute data-driven scenario and strategic planning - Quantify the adoption and downstream impact of deployed tools, driving iterative improvements Visualization & communication - Build compelling visualizations and dashboards that make complex research findings accessible to diverse audiences - Present research findings to senior leadership with clear, actionable recommendatio

ABOUT THE TEAM Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects OpenAI’s technology, people, and products. We are technical in what we build but are operational in how we do our work, and are committed to supporting all products and research at OpenAI. Our Security team tenets include: prioritizing for impact, enabling researchers, preparing for future transformative technologies, and engaging a robust security culture. ABOUT THE ROLE We’re seeking an exceptional Staff - Principal level offensive security domain expert to build agents that continuously identify and coordinate remediation of vulnerabilities across OpenAI’s infrastructure and applications. You will be the technical owner of this effort, combining deep offensive security judgment with agent engineering to build a production system that can operate safely and reliably at scale. As OpenAI increasingly uses automation throughout the company, we believe our security testing must become increasingly automated as well. Advances in model capabilities create an opportunity to test more of our attack surface than would be possible through human effort alone and a need to ensure that we remain ahead of those same capabilities as they become available to attackers. In this role, you’ll build a portfolio of specialized agents that develop a deep understanding of OpenAI’s infrastructure, applications, processes, and security boundaries. These agents will combine internal context with feedback from running systems to explore our cloud environments, Kubernetes clusters, web applications, endpoints, external attack surface, and other high-value targets. The goal is for agents to not only discover vulnerabilities, but also to validate exploitability, document impact, drive remediation, and verify fixes. Success will be measured through outcomes like vulnerabilities fixed, attack surface covered, and performance on evals you’ll build. These systems will operate continuously and with increasing autonomy, while using carefully designed guardrails and human-in-the-loop controls for dangerous actions. They will also learn from feedback from other domain experts throughout the company. IN THIS ROLE, YOU WILL: - Serve as the technical owner of OpenAI’s offensive security agents, establishing its architecture, technical direction, operating model, and evaluation strategy. - Design and build a portfolio of specialized agents that continuously test OpenAI’s infrastructure and applications from a variety of authenticated and unauthenticated perspectives. - Translate expert offensive security workflows and intuition into tools, skills, harnesses, policies, and internal knowledge bases. - Build agents that deeply understand OpenAI’s environment by integrating internal context. - Develop capabilities for testing cloud and Kubernetes environments, modern web applications, external attack surface, endpoints, and other high-value systems. - Build complete vulnerability-management loops that move beyond discovery to impact validation, ownership identification, prioritization, remediation support, progress tracking, and fix verification. - Design human-in-the-loop systems that allow offensive security engineers to approve or reject potentially dangerous actions, provide missing context, redirect investigations, and steer agents away from unproductive paths. - Create feedback mechanisms that allow agents to learn from the decisions, corrections, and domain expertise of experienced offensive security practitioners. - Develop rigorous evaluations that measure meaningful security outcomes and improvements in agent capability over time. - Build production-quality infrastructure that allows the system to run continuously, recover from failures, remain observable and debuggable, and operate safely against production systems. - Investigate failures in agent reasoning and behavior, identify where models are

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pretraining team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities: - Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development - Independently lead small research projects while collaborating with team members on larger initiatives - Design, run, and analyze scientific experiments to advance our understanding of large language models - Optimize and scale our training infrastructure to improve efficiency and reliability - Develop and improve dev tooling to enhance team productivity - Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications: - Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field - Strong software engineering skills with a proven track record of building complex systems - Expertise in Python and experience with deep learning frameworks (PyTorch preferred) - Familiarity with large-scale machine learning, particularly in the context of language models - Ability to balance research goals with practical engineering constraints - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment - Care about the societal impacts of your work Preferred Experience: - Work on high-performance, large-scale ML systems - Familiarity with GPUs, Kubernetes, and OS internals - Experience with language modeling using transformer architectures - Knowledge of reinforcement learning techniques - Background in large-scale ETL processes You'll thrive in this role if you: - Have significant software engineering experience - Are results-oriented with a bias towards flexibility and impact - Willingly take on tasks outside your job description to support the team - Enjoy pair programming and collaborative work &l

Perplexity is an AI-powered answer engine founded in December 2022 and growing rapidly as one of the world's leading AI platforms. Perplexity has raised over $1B in venture investment from some of the world's most visionary and successful leaders, including Elad Gil, Daniel Gross, Jeff Bezos, Accel, IVP, NEA, NVIDIA, Samsung, and many more. Our objective is to build accurate, trustworthy AI that powers decision-making for people and assistive AI wherever decisions are being made. Throughout human history, change and innovation have always been driven by curious people. Today, curious people use Perplexity to answer more than 780 million queries every month—a number that's growing rapidly for one simple reason: everyone can be curious. ABOUT THE ROLE Perplexity is building the AI platform for professional finance: Perplexity Computer, our agentic platform that navigates tools, completes multi-step workflows, and connects to the market data, filings, transcripts, and research that investment professionals already rely on, and our API Platform, which powers search, retrieval, and automation across structured and unstructured financial data. At the center of this is our Applied AI / Forward Deployed motion: we work closely with buyside firms—hedge funds, private equity, and asset managers—to distill their core investment processes and embed them into bespoke solutions built on our platform. That means sitting with analysts and PMs to understand how they screen, diligence, monitor, and decide, then translating those workflows into custom skills, agents, and connector configurations that run inside their daily process—and feeding what we learn back into the product so it compounds across customers. As an AI Strategist on our Applied AI team focused on financial services, you are the subject matter expert who makes this real for investment professionals. This is a commercial and sales engineering role: you will own strategic customer engagements end to end—leading discovery, defining what to build, demonstrating frontier capabilities against real investment workflows, and driving deployments from first conversation through production and expansion. You are part customer owner, part product strategist, part sales engineer—fluent in both the language of the deal team and the language of the platform. RESPONSIBILITIES OWN STRATEGIC FINANCIAL-SERVICES ENGAGEMENTS - Lead discovery with banks, PE firms, hedge funds, and asset managers: what matters to the investment team, what problems they are solving, and which workflows are worth automating - Define scope—what is the right thing to build—and own success metrics, pilot design, and commercial structure - Drive deployment and rollout across front-office teams, with real revenue responsibility alongside Sales BE THE DEMO AND TECHNICAL PROOF ENGINE - Build and deliver compelling, workflow-specific demonstrations: screening, comps, diligence, earnings analysis, research synthesis, portfolio monitoring - Prototype workflows, skills, and connector configurations hands-on in Perplexity Computer and the API Platform to prove value in the customer's own use cases - Run pilots and evaluations that hold up to scrutiny from skeptical analysts and PMs BRING DOMAIN JUDGMENT TO THE PRODUCT - Translate how deal teams, research desks, and portfolio managers actually work into product, connector, and data-partnership priorities - Assess whether we have the right market data, filings, transcripts, and research integrations to serve each finance sub-vertical—and drive the roadmap where we do not - Codify repeatable engagement patterns: discovery templates, finance-specific reference workflows, demo libraries, and deployment playbooks OPERATE AS THE COMMERCIAL LEAD - Own senior relationships with investment professionals and technology leaders; move engagements through qualification, scoping, proposal, pricing, legal, and close - Create urgency, write crisp follow-ups, and know when to push and when to wait Q