Anthropic

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 Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. About the role As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity. Responsibilities: - Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments - Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities - Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI. - Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows - Collaborate to translate experimental requirements into production-ready infrastructure - Develop large scale data pipelines to handle advanced language model training requirements - Optimize large scale training and inference pipelines for stable and efficient reinforcement learning You may be a good fit if you: - Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems - Are a strong communicator and enjoy working collaboratively - Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads - Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale - Have proven track record of building large-scale data pipelines and distributed storage systems - Excel at diagnosing and resolving complex infrastructure challenges in production environments - Can work effectively across the full ML stack from data pipelines to performance optimization - Have experience collaborating with other researchers to scale experimental ideas - Thrive in fast-paced environments and can rapidly iterate from experimentation to production Strong candidates may also have: - Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.) - Background in building infrastructure for AI research labs or large-scale ML organizations - Knowledge of GPU/TPU architectures and language mod

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 As a Senior Research Scientist on our Reward Models team, you'll lead research efforts to improve how we specify and learn human preferences at scale. Your work will directly shape how our models understand and optimize for what humans actually want — enabling Claude to be more useful, more reliable, and better aligned with human values. This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking. You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety. We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources. Your work will directly advance the science of how we train AI systems to be both highly capable and safe. Note: For this role, we conduct all interviews in Python. Responsibilities - Lead research on novel reward model architectures and training approaches for RLHF - Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability - Research techniques to detect, characterize, and mitigate reward hacking and specification gaming - Design experiments to understand reward model generalization, robustness, and failure modes - Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines - Contribute to research publications, blog posts, and internal documentation - Mentor other researchers and help build institutional knowledge around reward modeling You may be a good fit if you - Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning - Have experience training and evaluating reward models for large language models - Are comfortable designing and running large-scale experiments with significant computational resources - Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor - Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences - Care deeply about building AI systems that are both highly capable and safe Strong candidates may also - Have published research on reward modeling, preference learning, or RLHF - Have experience with LLM-as-judge approaches, including calibration and reliabili

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

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 Machine Learning Infrastructure Engineer to join our Safeguards organization, where you'll build and scale the critical infrastructure that powers our AI safety systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale. As part of the Safeguards team, you'll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable. Responsibilities: - Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem - Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications - Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems - Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards - Implement automated testing, deployment, and rollback systems for ML models in production safety applications - Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs - Contribute to the development of internal tools and frameworks that accelerate safety research and deployment You may be a good fit if you: - Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment - Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX - Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes) - Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads - Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems) - Are results-oriented, with a bias towards reliability and impact in safety-critical systems - Enjoy collaborating with researchers and translating cutting-edge research into production systems - Care deeply about AI safety and the societal impacts of your work Strong candidates may have experience with: - Working with large language models and modern transformer architectures - Implementing A/B testing frameworks and experimentation infrastructure for ML systems - Developing monitoring and alerting systems for ML model performance and data drift - Building automated labeling systems and human-in-the-loop workflows - Ex

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’re looking for prompt and context engineers to join our product engineering team to help build AI-first products, features, and evaluations. Your mission will be to bridge the gap between model capabilities and real product experience, working with product teams to build consistent, safe, and beneficial user experiences across all product surfaces. You will be deeply involved in new product feature and model releases at Anthropic, combining engineering expertise with an understanding of frontier AI applications and model quality. You’ll become an expert on Claude’s behavioral quirks and capabilities and apply that knowledge to deliver the best possible user experience across models and domains. You’ll be the first resource for product teams working on Claude’s AI infrastructure: system prompts, tool prompts, skills, and evaluations. This role requires someone who can effectively balance caring deeply about making Claude the best it can be while also supporting a wide variety of concurrent projects and efforts across many product teams. Key Responsibilities - Prompt Engineering Excellence: Design, test, and optimize system prompts and feature-specific prompts that shape Claude’s behavior across consumer and API products. - Evaluation Development: Build and maintain comprehensive evaluation suites that ensure model quality and consistency across product launches and updates. - Cross-functional Collaboration: Partner closely with product teams, research teams, and safeguards to ensure new features meet quality and safety standards. - Model Launch Support: Play a critical role in model releases, ensuring smooth rollouts and catching regressions before they impact users. - Infrastructure Contribution: Help build and improve the frameworks and tools that allow teams to develop and test prompts and features with confidence. - Knowledge Transfer: Mentor product engineers on prompt engineering best practices and help teams build their first evaluations. - Rapid Iteration: Work in a fast-paced environment where model capabilities advance daily, requiring quick adaptation and creative problem-solving. What We’re Looking For Required Qualifications - 5+ years of software engineering experience with Python or similar languages. - Demonstrated experience with LLMs and prompt engineering (through work, research, or significant personal projects). - Strong understanding of evaluation methodologies and metrics for AI systems. - Excellent written and verbal communication skills – you’ll need to explain complex model behaviors to diverse stakeholders. - Ability to manage multiple concurrent projects and prioritize effectively. - Experience with version control, CI/CD, and modern software development practices. &lt

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. You want to build large scale ML systems from the ground up. You care about making safe, steerable, trustworthy systems. As a Research Engineer, you'll touch all parts of our code and infrastructure, whether that's making the cluster more reliable for our big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling. You're excited to write code when you understand the research context and more broadly why it's important. Note: This is an "evergreen" role that we keep open on an ongoing basis. We receive many applications for this position, and you may not hear back from us directly if we do not currently have an open role on any of our teams that matches your skills and experience. We encourage you to apply despite this, as we are continually evaluating for top talent to join our team. You are also welcome to reapply as you gain more experience, but we suggest only reapplying once per year. We may also put up separate, team-specific job postings . In those cases, the teams will give preference to candidates who apply to the team-specific postings, so if you are interested in a specific team please make sure to check for team-specific job postings! You may be a good fit if you: - Have significant software engineering experience - 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 learn more about machine learning research - Care about the societal impacts of your work Strong candidates may also have experience with: - High performance, large-scale ML systems - GPUs, Kubernetes, Pytorch, or OS internals - Language modeling with transformers - Reinforcement learning - Large-scale ETL Representative projects: - Optimizing the throughput of a new attention mechanism - Comparing the compute efficiency of two Transformer variants - Making a Wikipedia dataset in a format models can easily consume - Scaling a distributed training job to thousands of GPUs - Writing a design doc for fault tolerance strategies - Creating an interactive visualization of attention between tokens in a language model 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

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 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 As a Senior Research Scientist on our Reward Models team, you'll lead research efforts to improve how we specify and learn human preferences at scale. Your work will directly shape how our models understand and optimize for what humans actually want — enabling Claude to be more useful, more reliable, and better aligned with human values. This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking. You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety. We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources. Your work will directly advance the science of how we train AI systems to be both highly capable and safe. Note: For this role, we conduct all interviews in Python. Responsibilities - Lead research on novel reward model architectures and training approaches for RLHF - Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability - Research techniques to detect, characterize, and mitigate reward hacking and specification gaming - Design experiments to understand reward model generalization, robustness, and failure modes - Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines - Contribute to research publications, blog posts, and internal documentation - Mentor other researchers and help build institutional knowledge around reward modeling You may be a good fit if you - Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning - Have experience training and evaluating reward models for large language models - Are comfortable designing and running large-scale experiments with significant computational resources - Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor - Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences - Care deeply about building AI systems that are both highly capable and safe Strong candidates may also - Have published research on reward modeling, preference learning, or RLHF - Have experience with LLM-as-judge approaches, including calibration and reliabili

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 production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models. Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends. Responsibilities: - Implement and optimize post-training techniques at scale on frontier models - Conduct research to develop and optimize post-training recipes that directly improve production model quality - Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation - Develop tools to measure and improve model performance across various dimensions - Collaborate with research teams to translate emerging techniques into production-ready implementations - Debug complex issues in training pipelines and model behavior - Help establish best practices for reliable, reproducible model post-training You may be a good fit if you: - Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities - Adapt quickly to changing priorities - Maintain clarity when debugging complex, time-sensitive issues - Have strong software engineering skills with experience building complex ML systems - Are comfortable working with large-scale distributed systems and high-performance computing - Have experience with training, fine-tuning, or evaluating large language models - Can balance research exploration with engineering rigor and operational reliability - Are adept at analyzing and debugging model training processes - Enjoy collaborating across research and engineering disciplines - Can navigate ambiguity and make progress in fast-moving research environments Strong candidates may also: - Have experience with LLMs - Have a keen interest in AI safety and responsible deployment We welcome candidates at various

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 Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models' abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows. About the role As a Research Engineer on our team you will work end to end, identifying and addressing key blockers on the path to scientific AGI. Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively. Familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged. Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity. Responsibilities: - Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI - Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery - Scaling research ideas from prototype to production - Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use - Implement distributed training systems and performance optimizations to support large-scale model development You may be a good fit if you: - Have 8+ years of ML research experience - Are familiar with large scale language model training, evaluation, and inference pipelines - Enjoy obsessively iterating on immediate blockers towards longterm goals - Thrive working collaboratively to solve problems - Have expertise in performance optimization and distributed computing systems - Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems - Can translate research concepts into scalable engineering solutions - Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems Strong candidates may also have: - Expertise with performance optimization for language model inference and training - Experience with computer use automation and agentic AI systems - A history working on reinforcement learning approaches for complex task completion - Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale - Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing) - Have experience with VM/sandboxing/container deployment and large-scale data processing - Expe

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 Safeguards Labs is a new team operating at the intersection of research and engineering, chartered to investigate novel safety methods that protect Claude and the people who use it. We prototype new approaches to safe models, usage safeguards, and production safety, pressure-testing ideas before they graduate into production systems run by our partner Safeguards teams. We're hiring software engineers to partner with our research engineers and turn promising prototypes into reliable, production-grade safeguards. The team is small, so each engineer has substantial latitude over what they work on and high leverage on the team's direction. Key responsibilities - Take research prototypes and harden them into production services that integrate with Anthropic's real-time safeguards path. - Build data and evaluation infrastructure that lets the team iterate on prototypes quickly and measure whether safeguards actually work, including in agentic settings. - Own deployment, monitoring, and reliability for systems Labs ships. - Build internal tooling that helps investigators surface and act on abuse patterns. - Collaborate with research engineers on scoping and contribute to decisions about which prototypes are ready to graduate. Minimum qualifications - Strong proficiency in Python and comfort working with large datasets. - A track record of designing, building, and operating production backend systems or data pipelines. - Experience taking software from prototype to production, including testing, monitoring, and reliability work. - Working familiarity with how large language models operate, even if LLMs aren't your primary background. - Care about the societal impacts of AI and want your work to directly reduce real-world harm. Preferred qualifications - At least 5 years of software engineering experience. - Experience deploying ML systems or classifiers into production. - Background in trust and safety, integrity, fraud detection, threat intelligence, or adversarial ML. - Experience building developer-facing tooling or platforms that accelerate research workflows. - Familiarity with evaluation methodologies for language models. - Experience with agentic environments. - A history of partnering with researchers and successfully transferring prototypes into production. The annual compensation range for this role is listed below. </p

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

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 As a Research Engineer / Scientist on the Societal Impacts team, you'll design and build critical infrastructure that enables and accelerates foundational research into how our AI systems impact people and society. Your work will directly contribute to our research publications, policy campaigns, safety systems, and products. Our team combines rigorous empirical methods with creative technical approaches. We’re currently grappling with big questions on how AI might impact the future of work , people's wellbeing , education , and more. Additionally, we are continuously studying socio-technical alignment (what values do our systems have?), and evaluating novel AI capabilities as they arise. We develop privacy-preserving tools to measure AI's effects at scale, conduct mixed-methods studies of human-AI interaction, and translate research insights into actionable recommendations for both product and policy. You can learn more about our team here Strong candidates will have a track record of running & designing experiments relating to machine learning systems, building data processing pipelines, architecting & implementing high-quality internal infrastructure, working in a fast-paced startup environment, navigating the ambiguity inherent to novel empirical research, and demonstrating an eagerness to develop their own research & technical skills. The ideal candidate will enjoy a mixture of running experiments, developing new tools & evaluation suites, working cross-functionally across multiple research and product teams, and striving for beneficial & safe uses for AI. Responsibilities: - Design and implement scalable technical infrastructure that enables researchers to efficiently run experiments and evaluate AI systems. - Architect systems that can handle uncertain and changing requirements while maintaining high standards of reliability - Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions - Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission - Interface with and improve our internal technical infrastructure and tools - Generate net-new insights about the potential societal impact of systems being developed by Anthropic - Ship changes that help improve our models and products based on the empirical research the Societal Impacts team is conducting </li

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 production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models. Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends. Responsibilities: - Implement and optimize post-training techniques at scale on frontier models - Conduct research to develop and optimize post-training recipes that directly improve production model quality - Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation - Develop tools to measure and improve model performance across various dimensions - Collaborate with research teams to translate emerging techniques into production-ready implementations - Debug complex issues in training pipelines and model behavior - Help establish best practices for reliable, reproducible model post-training You may be a good fit if you: - Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities - Adapt quickly to changing priorities - Maintain clarity when debugging complex, time-sensitive issues - Have strong software engineering skills with experience building complex ML systems - Are comfortable working with large-scale distributed systems and high-performance computing - Have experience with training, fine-tuning, or evaluating large language models - Can balance research exploration with engineering rigor and operational reliability - Are adept at analyzing and debugging model training processes - Enjoy collaborating across research and engineering disciplines - Can navigate ambiguity and make progress in fast-moving research environments Strong candidates may also: - Have experience with LLMs - Have a keen interest in AI safety and responsible deployment We welcome candidates at various

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 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 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 As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same. Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage. This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission. Responsibilities: - Build new novel and long-horizon evaluations - Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training - Lead strategic evaluation coverage across the company - Shape the evaluation narrative for model releases - Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research - Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules - Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions - Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices You may be a good fit if you: - Have significant experience designing and running evaluations for large language models or similar complex ML systems - Have led technical projects or teams, either formally or through sustained ownership of critical research directions - Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly - Think strategically about what to measure and why, not just how to measure it - Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities - Communicate complex technical findings clearly to both technical and non-technical audiences - Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings <l

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’s Audio team pushes the boundaries of what's possible with audio with large language models. We care about making safe, steerable, reliable systems that can understand and generate speech and audio, prioritizing not only naturalness but also steerability and robustness. As a researcher on the Audio team, you'll work across the full stack of audio ML, developing audio codecs and representations, sourcing and synthesizing high quality audio data, training large-scale speech language models and large audio diffusion models, and developing novel architectures for incorporating continuous signals into LLMs. Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. The team works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high impact real-world deployments. You may be a good fit if you: - Have hands-on experience with training audio models, whether that's conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, or generative audio models - Genuinely enjoy both research and engineering work, and you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other - Are comfortable working across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization - Have deep expertise with JAX, PyTorch, or large-scale distributed training, and can debug performance issues across the full stack - Thrive in fast-moving environments where the most important problem might shift as we learn more about what works - Communicate clearly and collaborate effectively; audio touches many parts of our systems, so you'll work closely with teams across the company - Are passionate about building conversational AI that feels natural, steerable, and safe - Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly Strong candidates may also have experience with: - Large language model pretraining and finetuning - Training diffusion models for image and audio generation - Reinforcement learning for large language models and diffusion models - End-to-end system optimization, from performance benchmarking to kernel optimization - GPUs, Kubernetes, PyTorch, or distributed training infrastructure Representative projects: - Training state-of-the art neural audio codecs for 48 kHz stereo audio - Developing novel algorithms for diffusion pretraining and reinforcement learning - Scaling audio datasets to millions of hours of high quality audio - Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressivene

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 Safeguards Labs is a new team operating at the intersection of research and engineering, chartered to investigate novel safety methods that protect Claude and the people who use it. We prototype new approaches to safe models, usage safeguards, and production safety — pressure-testing ideas through offline analysis and subsets of traffic before they graduate into production systems run by our partner Safeguards teams. Our work overlaps closely with account abuse, model behavior safeguards, and other safeguard subteams, and we serve as a research arm that can take on ambitious, ambiguous problems and turn them into deployed defenses. About the Role We're hiring research engineers to define and execute the Labs research agenda. You'll scope your own projects, run experiments end-to-end, and decide when an idea is ready to hand off to a production team — or when to kill it and move on. The team is small and being built deliberately around a roughly 3:1 mix of researchers to software engineers, so each person has substantial latitude over what they work on and high leverage on the team's direction. Responsibilities: - Lead and contribute to research projects investigating new methods for detecting misuse of Claude, identifying malicious organizations and accounts, strengthening model safeguards, and other safety needs. - Design and run offline analyses over model usage data to surface abuse patterns, build classifiers and detection systems, and evaluate their effectiveness. - Develop and iterate on prototypes that could eventually feed signals into the real-time safeguards path, partnering with engineers on tech transfer. - Contribute to a broader research portfolio investigating methods for detecting abusive behavior in chat-based or agentive workflows, and for training the model to robustly refrain from dangerous responses or behaviors without over-refusing. - Build evaluations and methodologies for measuring whether safeguards actually work, including in agentic settings. - Write up findings clearly so they inform decisions across Trust & Safety, research, and product teams. You may be a good fit if you: - Have a track record of independently driving research projects from ambiguous problem statements to concrete results, ideally in AI, ML, security, integrity, or a related technical field. - Are comfortable scoping your own work and switching between research, engineering, and analysis as a project demands. - Have working familiarity with how large language models operate — sampling, prompting, training — even if LLMs aren't your primary background. - Are proficient in Python and comfortable working with large datasets. - Care about the societal impacts of AI and want your work to directly reduce real-world harm. Strong candidates may also have: - Experience building and training machine learning models, including classifiers for abuse, fraud, integrity, or security applications. - Knowledge

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

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

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 researchers to help mitigate the risks that come with building AI systems. One of these risks is the potential for models to interact with private user data. In this role, you'll design and implement privacy-preserving techniques, audit our current techniques, and set the direction for how Anthropic handles privacy more broadly. Responsibilities: - Lead our privacy analysis of frontier models, carefully auditing the use of data and ensuring safety throughout the process - Develop privacy-first training algorithms and techniques - Develop evaluation and auditing techniques to measure the privacy of training algorithms - Work with a small, senior team of engineers and researchers to enact a forward-looking privacy policy - Advocate on behalf of our users to ensure responsible handling of all data You may be a good fit if you have: - Experience working on privacy-preserving machine learning - A track record of shipping products and features inside a fast-moving environment - Strong coding skills in Python and familiarity with ML frameworks like PyTorch or JAX. - Deep familiarity with large language models, how they work, and how they are trained - Have experience working with privacy-preserving techniques (e.g., differential privacy and how it is different from k-anonymity, l-diversity, and t-closeness) - Experience supporting fast-paced startup engineering teams - Demonstrated success in bringing clarity and ownership to ambiguous technical problems - Proven ability to lead cross-functional security initiatives and navigate complex organizational dynamics Strong candidates may also: - Have published papers on the topic of privacy-preserving ML at top academic venues - Prior experience training large language models (e.g., collecting training datasets, pre-training models, post-training models via fine-tuning and RL, running evaluations on trained models) - Prior experience developing tooling to support privacy-preserving ML (e.g., differential privacy in TF-Privacy or Opacus) 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: $320,000 - $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or 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. 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 The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows — searching, analyzing, and creating across the tools and documents knowledge workers use every day. As that work scales, the systems behind it need to be as rigorous as the research itself. We are looking for a Research Engineer to own the reliability, observability, and infrastructure foundation that the team's research depends on. You will be responsible for ensuring our training and evaluation runs remain stable, well-instrumented, and high-quality as they grow in scale and complexity. A core part of this role is shifting reliability work from reactive to proactive: hardening systems, stress-testing at realistic scale, and building the observability and tooling that surface problems early — so researchers can stay focused on research rather than incident response. You will be the team's stable, context-rich owner for environment health and evaluation integrity, and the primary point of contact for partner teams when issues arise. Where this role focuses: While you'll work closely with researchers building new training environments, the priority for this role is the reliability those environments depend on. It's best suited to an engineer who finds real ownership and impact in making critical systems dependable, and in being the person behind trustworthy evaluation results the entire organization relies on. Key Responsibilities: - Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership - Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases - Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation - Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale, so failures surface early rather than during critical training work - Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution - Reduce the operational burden on researchers so they can stay focused on research Minimum Qualifications: - Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production - Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience - Strong SRE or production-engineering mindset — reaching for SLOs, load tests, and failure injection before reaching for more dashboards <li

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: 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 frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax) - Published research on model training, scaling laws, or ML systems - Experience with production ML systems, observability tools, or evaluation infrastructure - Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence What Makes This Role Unique: This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends. However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding. We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you. Location: This role requires working in-office 5 days per week in London. Deadline to apply: None. Applications will be reviewed on a rolling basis. 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: £260,000 - £630,000 GBP 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 experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

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 frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax) - Published research on model training, scaling laws, or ML systems - Experience with production ML systems, observability tools, or evaluation infrastructure - Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence What Makes This Role Unique: This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends. However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding. We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you. Location: This role requires working in-office 5 days per week in San Francisco. Deadline to apply: None. Applications will be reviewed on a rolling basis. 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 experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

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 environment - Able to work independently while collaborating tightly with research, product, and domain-expert teams - Results-oriented with a bias toward rapid iteration and measurable impact - Passionate about using AI to accelerate scientific discovery while maintaining high ethical standards Preferred Qualifications - 5+ years of experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar - Ph.D. in computational biology, bioinformatics, bioengineering, CS, or a related quantitative field — or equivalent industry experience - Experience with LLM post-training: RLHF, RL from verifiable rewards, SFT data curation, or eval-driven development - Direct experience with therapeutic discovery pipelines — target identification, lead optimization, ADMET modeling, or clinical data analysis - Familiarity with bioinformatics tooling and pipelines (sequence analysis, structure prediction, single-cell, variant calling, etc.) - Experience building agentic systems or tool-use environments - Published research in ML for biology, or open-source contributions to computational biology tools - Fluency with biological databases (UniProt, PDB, Ensembl, NCBI) and the ability to reason about their schemas and failure modes 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: $300,000 - $320,000 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 experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Applied AI Architect, National Security at Anthropic. Source: greenhouse:anthropic.

Applied AI Architect, Industries at Anthropic. Source: greenhouse:anthropic.

Applied AI Architect, Industries at Anthropic. Source: greenhouse:anthropic.

Applied AI Architect, Industries at Anthropic. Source: greenhouse:anthropic.

Applied AI Architect, Industries at Anthropic. Source: greenhouse:anthropic.

Applied AI Architect, Government Technology at Anthropic. Source: greenhouse:anthropic.

Applied AI Architect, Federal Civilian at Anthropic. Source: greenhouse:anthropic.

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: As an Applied AI team member at Anthro

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. Location - Mumbai &nbs

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 As an Applied AI team member at Anthrop

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. Apply using this link . &lt

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. Apply using this link . &lt

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. Apply using this link . &lt

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 As frontier AI regulation matures globally — with the EU AI Act, evo

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: As an Applied AI Security Architect, you will serve as Anthropic's trusted security expert for our most demanding enterprise customers. You'll engage directly with CISOs, security architects, compliance officers, and technical leaders at the world's largest financial institutions, insurance companies, and other highly regulated enterprises

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 People Research Scientist to join our People Data Solutions team. You’ll be the research expert supporting our broader People organization, using rigorous scientific methods to advance our understanding of the employee experience, manager effectiveness, organizational health, and workforce dynamics. This role sits at the intersection of organizational science, behavioral re

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: As a member of the Applied AI team at Anthropic, you will be a Forward Deployed Engineer (FDE) who embeds directly with our most strategic customers to drive transformational AI adoption. You will collaborate closely with customer teams to ship advanced AI applications that solve real world business problems. Our FDEs engage with customers to accelerate the adop

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:&nbsp; As the manager of the Applied AI Architect, Enterprise Tech (Cyber) team at Anthropic, you will drive the adoption of frontier AI by enabling the deployment of Anthropic's products (Claude for Enterprise, Claude Code, and API) across Enterprise Tech companies and digital-first organizations. You'll leverage your technical skills and

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 production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models. Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends. Responsibilities: - Implement and optimize post-training techniques at scale on frontier models - Conduct research to develop and optimize post-training recipes that directly improve production model quality - Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation - Develop tools to measure and improve model performance across various dimensions - Collaborate with research teams to translate emerging techniques into production-ready implementations - Debug complex issues in training pipelines and model behavior - Help establish best practices for reliable, reproducible model post-training You may be a good fit if you: - Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities - Adapt quickly to changing priorities - Maintain clarity when debugging complex, time-sensitive issues - Have strong software engineering skills with experience building complex ML systems - Are comfortable working with large-scale distributed systems and high-performance computing - Have experience with training, fine-tuning, or evaluating large language models - Can balance research exploration with engineering rigor and operational reliability - Are adept at analyzing and debugging model training processes - Enjoy collaborating across research and engineering disciplines - Can navigate ambiguity and make progress in fast-moving research environments Strong candidates may also: - Have experience with LLMs - Have a keen interest in AI safety and responsible deployment We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role. 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: 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 experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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 As a Research Engineer on the Economic Research team, you will design, build, maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis . The Economic Research team at Anthropic studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data that is clear-eyed about the economic effects of AI to help policymakers, businesses, and the public understand and navigate the transition to powerful AI. We use our insights to inform Anthropic decisions internally across the business. In this role, you will work closely with teams across Anthropic—including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy—to build scalable and robust data systems that support high-leverage, high-impact research. Strong candidates will have a track record building data processing pipelines, architecting & implementing high-quality internal infrastructure, working in a fast-paced startup environment, navigating ambiguity, and demonstrating an eagerness to develop their own research & technical skills. Responsibilities: - Build and maintain data pipelines that process large scale Claude usage logs into canonical, reusable datasets while maintaining user privacy. - Expand privacy-preserving tools to enable new analytic functionality to support research needs. - Design and implement novel data systems leveraging language models (e.g., CLIO ) where traditional software engineering patterns don't yet exist. - Develop and maintain data pipelines that are interoperable across data sources (including ingesting external data) and are designed to support economic analysis. - Contribute to the strategic development of the economic research data foundations roadmap - Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure - Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions - Create documentation and best practices that enable self-serve data access for researchers while maintaining security and governance standards. - Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission You might be a good fit if you have: - Have experience working with Research Scientists and Economists on ambiguous AI and economic projects - Have experience with building and maintaining data infrastructure, large datasets, and internal tools in production environments. - Have experience with cloud infrastructure platforms such as AWS or GCP. - Take pride in writing clean, well-documented code in Python that others can build upon - Are comfortable making technical decisions with incomplete information while maintaining high engineering standards - Are comfortable getting up-to-speed quickly on unfamiliar codebases, and can work well with other engineers with different backgrounds across the organization - Have a track record of using technical infrastructure to interface effectively with machine learning models - Have experience deriving insights from imperfect data streams - Have experience building systems and products on top of LLMs - Have experience incubating and maturing tooling platforms used by a wide variety of stakeholders - A passion for Anthropic's mission of building helpful, honest, and harmless AI and understanding its economic implications. - A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description. - Strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise. Strong candidates may have: - Background in econometrics, statistics, or quantitative social science research - Experience building data infrastructure and data foundations for research - Familiarity with large language models, AI systems, or ML research workflows - Prior work on projects related to labor economics, technology adoption, or economic measurement Some Examples of Our Recent Work - Anthropic Economic Index Report: Economic Primitives - Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption - Estimating AI productivity gains from Claude conversations - The Anthropic Economic Index Deadline to apply: None. Applications are reviewed on a rolling basis 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: $300,000 - $405,000 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 experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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 As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world. Responsibilities: - Define core metrics, build measurement frameworks, and maintain core reporting to evaluate success - Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through your insights and recommendations - Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls – and analyze the results in order make actionable recommendations - Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions - Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes - Present complex technical analyses and recommendations to both technical and non-technical stakeholders - Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow You may be a good fit if you have: - 7+ years of experience in data science or analytics roles - Deep expertise with Python, SQL, and data visualization tools - Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments - Highly effective written communication and presentation skills - A track record of translating complex data into clear, actionable insights for both technical and business stakeholders - A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress - A passion for the company’s mission of building helpful, honest, and harmless AI - Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem We’re hiring across multiple pillars Applying for this role will allow you to be considered for all pillars currently hiring. You will be asked to select a preference when submitting an application. Claude Code This role is embedded with the Claude Code product team, driving data-informed decisions for Anthropic's agentic coding tool that enables developers to delegate coding tasks directly to Claude from their terminal. You'll help the team understand how developers interact with AI coding assistants, measure developer productivity and product quality, and identify opportunities to innovate the developer experience as the product scales. Key focus areas include developer usage patterns across the platform, driving adoption within the developer ecosystem, and developer segmentation. Strong candidates may have: - Deep familiarity with software development workflows, developer tools, and engineering productivity metrics—ideally from working at developer-focused companies or on products targeting software engineers - Experience analyzing human-AI interaction patterns, particularly in code generation or developer tooling contexts - Experience supporting product teams building for the Enterprise Consumer Data Scientist You will be embedded with our Consumer product team. This team is responsible for building all consumer-facing Claude experiences—including web, mobile, desktop, and browser extensions. In this role, you'll shape how millions of users interact with Claude daily, driving product insights to product recommendations for interfaces that are intuitive, responsive, and push the boundaries of what AI-powered applications can be. Strong candidates may have: - Experience working closely with Product and Engineering teams on consumer products across multiple platforms (web, iOS, Android, desktop, browser extensions) - Demonstrated impact on product roadmap and strategy from data deep dives in consumer growth, engagement and retention - Expert in experimentation, holding a high statistical bar for measuring the impact of core product changes Platform Product Data Scientist You will partner closely with product, engineering, and go-to-market teams to understand how developers and enterprise customers build on and adopt the Claude Developer Platform—spanning our core API, agent orchestration, tool and MCP integrations, and knowledge management capabilities. You'll identify growth opportunities, surface insights about how AI agents are being built and deployed at scale, and drive data-informed decisions that shape our platform roadmap. Strong candidates may have: - 3+ years of experience working closely with Product or Engineering teams on API or developer-facing products, with demonstrated impact on product roadmap and strategy - Experience supporting B2B sales teams with data insights - Strong instincts for what drives product adoption, engagement, and retention Model Experience Data Scientist You will work closely with product, engineering, and research leaders to bring data-driven rigor to every phase of model development and launch. Sitting at the true bleeding edge of putting frontier AI research into the public domain, you will leverage data from both external customers and internal testing to define and measure key company success metrics, and analyze user and model behavior to identify new opportunities to push the frontier. Strong candidates may have: - 3+ years of experience deeply embedding in Product or Research teams, preferably working with LLM or AI products - Comfort working with unstructured data 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: $275,000 - $370,000 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 experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process