Anthropic has 145 active AI-related job listings. The majority of these roles are focused on agents, comprising 28% of the total. Engineering is the most frequent function, with 74 listings, followed by Research with 51. The company is primarily hiring in the United States, with 118 positions, and the United Kingdom, with 22. Frequent tech tags include model_serving, evals, and agent_orchestration, suggesting a focus on deployment and evaluation of AI systems. In the last 30 days, Anthropic posted 16 new AI roles, a 47% decrease compared to the previous 30-day period.
Currently tracking 124 active AI roles, with 106 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $46k–$850k (avg $405k).
Anthropic currently has 132 active AI-related roles in our index. The most common open titles are: Applied AI Architect, Industries (2), Regional Research Economist, Economic Research (2), Research Engineer, Machine Learning (RL Velocity) (2), Research Engineer, Production Model Post-Training (2), Staff Software Engineer, AI Reliability Engineering (2). Most positions are in Engineering and Research.
Anthropic's active AI hiring is concentrated in: agents (28%), serving infrastructure (17%), post-training (14%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Anthropic is hiring AI talent in: United States (106 roles), United Kingdom (20 roles), Canada (6 roles), Ireland (5 roles).
Job postings at Anthropic most frequently reference: model serving, evals, llm observability, agent orchestration, inference infra.
In the past 30 days, Anthropic has posted 29 new AI-related roles. That is a +61% change versus the prior 30 days (18 → 29).
| Title | Stage | AI score |
|---|---|---|
| Research Scientist, Interpretability Research Scientist focused on mechanistic interpretability of LLMs, aiming to understand how trained models work by reverse-engineering their parameters and algorithms. The role involves developing methods, designing experiments, creating interpretability features, building infrastructure, and collaborating with other teams. Requires strong scientific research background with some interpretability work, comfort with experimental science, and proficiency in Python. | Post-train | 10 |
| [Expression of Interest] Research Manager, Interpretability Research Manager for the Interpretability team, focusing on mechanistic interpretability to understand how large language models work internally and ensure AI safety. The role involves partnering with a research lead on direction, project planning, execution, hiring, and people development, translating research ideas into tangible goals, and overseeing their execution. This is a management role, distinct from individual contributor research scientist or engineer roles. |
| Post-train |
| 10 |
| Anthropic AI Safety Fellow, Canada This is a fellowship program focused on AI safety research, aiming to bridge industry engineering expertise with research skills. Fellows will work on empirical projects using external infrastructure, with the goal of producing public outputs like paper submissions. The program offers mentorship, funding, and compute resources. | Post-train | 10 |
| Anthropic AI Safety Fellow, US This is a fellowship program focused on AI safety research, aiming to bridge industry engineering expertise with research skills. Fellows will work on empirical projects using external infrastructure, with the goal of producing public outputs like paper submissions. The program offers mentorship, funding, and compute resources. | Post-train | 10 |
| Research Engineer, Machine Learning (Reinforcement Learning) Research Engineer focused on Reinforcement Learning to advance capabilities and safety of large language models. This role involves implementing novel approaches, contributing to research direction, creating agentic models via tool use for tasks like computer use and autonomous software generation, and improving reasoning abilities. Projects include architecting RL infrastructure, designing training environments and evaluations for RL agents, driving performance improvements, and developing automated testing frameworks. | Post-trainAgent | 10 |
| Research Engineer, Interpretability Research Engineer focused on mechanistic interpretability to understand and improve the safety of large language models. This involves implementing and analyzing experiments, optimizing research workflows, building tools for experimentation, and developing infrastructure to support model safety improvements. | Post-train | 10 |
| Research Manager, Interpretability Manager for the Interpretability team focused on mechanistic interpretability of large language models, aiming to understand how they work internally for AI safety. | Post-train | 10 |
| Research Scientist, Interpretability Research Scientist focused on mechanistic interpretability of LLMs, aiming to understand how neural network parameters map to algorithms for safety and steerability. Involves developing methods, running experiments, building infrastructure, and communicating results. | Post-train | 10 |
| Research Engineer, Code RL (Reinforcement Learning) Research Engineer focused on Reinforcement Learning for code generation, aiming to improve models' ability to write, edit, test, debug, and ship software. This role involves designing RL environments, building reward signals, running training experiments, and improving pipeline efficiency, blending research with engineering implementation. | Post-trainAgent | 9 |
| Research Scientist, Life Sciences Research Scientist role focused on improving AI model capabilities for life sciences tasks. This involves building agentic tools, designing evaluation benchmarks, and applying post-training techniques to enhance model performance on scientific workflows like bioinformatics, database queries, and literature synthesis. The role bridges ML, software engineering, and biology to make AI a better research assistant in life sciences. | Post-trainAgent | 9 |
| Technical Program Manager, Discovery Technical Program Manager on the Discovery team, owning systems and programs that determine research velocity, including compute planning, scientific RL environment health, and vendor pipelines. Requires ML engineering or research background with program leadership experience, technical depth to debug pipelines and analyze RL transcripts, and organizational effectiveness to coordinate across research, infrastructure, product, and data operations. | Post-trainData | 9 |
| Research Engineer, Search and Knowledge Post-Training Research Engineer focused on advancing search and knowledge capabilities in LLMs through post-training techniques. The role involves defining research hypotheses, designing experiments, building instrumentation for controlled studies, developing evaluations to distinguish reasoning from pattern matching, and driving optimization rigor. It sits at the intersection of RL, retrieval, and evaluation, aiming to make LLMs trustworthy searchers. | Post-trainAgent | 9 |
| Technical Program Manager, Research This role is a Technical Program Manager for Anthropic's research organization. The TPM will define and build programs for research teams, focusing on areas like compute, evals, and RL environments. They will drive end-to-end execution of complex research initiatives, establish processes, and ensure operational health of RL environments. The role requires a background in ML research or engineering, experience building technical programs from scratch, and the ability to navigate ambiguity in fast-moving research environments. | Post-trainData | 9 |
| Anthropic Fellows Program — Reinforcement Learning This is a research fellowship program focused on Reinforcement Learning (RL) within AI safety. Fellows will work on empirical projects, potentially using external infrastructure, with the goal of producing public outputs like paper submissions. The program emphasizes mentorship from Anthropic researchers and provides a stipend and compute funding. Key activities include building model-based tools for data quality, understanding generalization, and creating RL environments for capabilities and safety tasks. | Post-train | 9 |
| Anthropic Fellows Program — AI Safety This is a research fellowship program focused on AI safety, aiming to foster talent in empirical AI research. Fellows will work on projects aligned with Anthropic's research priorities, using external infrastructure and external models, with the goal of producing public outputs like paper submissions. Key research areas include Scalable Oversight, Adversarial Robustness and AI Control, Model Organisms, Model Internals / Mechanistic Interpretability, and AI Welfare. | Post-train | 9 |
| Research Engineer, Performance RL Research Engineer focused on Reinforcement Learning for code generation and accelerator performance, aiming to improve model reasoning and coding capabilities. The role involves inventing RL environments, conducting experiments, shaping research roadmaps, and delivering work into training runs, with a strong emphasis on collaboration and scaling research innovations. | Post-trainData | 9 |
| Research Engineer, Production Model Post-Training Research Engineer focused on post-training of production LLMs, implementing and optimizing techniques like Constitutional AI and RLHF to enhance model capabilities, alignment, and safety. Involves research, pipeline development, evaluation, and debugging at scale. | Post-train | 9 |
| Research Engineer / Research Scientist, Vision Research Engineer/Scientist focused on vision and spatial reasoning for LLMs, working on pretraining, RL, and runtime techniques like agentic harnesses. Involves developing and evaluating multimodal capabilities, creating benchmarks, and partnering with product teams to improve Claude models. | Post-trainAgent | 9 |
| Research Engineer/Research Scientist, Audio Research Engineer/Scientist focused on audio AI, working on training audio models, developing novel architectures, and optimizing inference for speech and audio understanding and generation systems. | Post-trainServe | 9 |
| Senior Research Scientist, Reward Models Senior Research Scientist focused on reward models for LLMs, involving novel architectures, RLHF, LLM-based evaluation, and mitigating reward hacking. Aims to improve model alignment with human values and translate research into production systems. | Post-trainEval Gate | 9 |
| Research Engineer, Reward Models Platform Research Engineer focused on building platforms and infrastructure to automate and accelerate the reward model development and evaluation workflows for ML researchers at Anthropic. The role involves creating tools for rubric development, human feedback analysis, reward robustness evaluation, and detecting reward hacks, with the goal of enabling rapid iteration and improving reward signal quality for training AI models. | Post-train | 9 |
| Anthropic Fellows Program — AI Security This is a research fellowship program focused on AI safety and security, aiming to produce public outputs like paper submissions. Fellows will use external infrastructure and open-source models, working on empirical projects with mentorship from Anthropic researchers. | Post-train | 9 |
| Research Engineer, Cybersecurity Reinforcement Learning Research Engineer role focused on applying reinforcement learning to cybersecurity tasks like secure coding and vulnerability remediation, blending research and engineering to train safe AI models. Requires cybersecurity domain expertise and ML/software engineering skills. | Post-trainData | 9 |
| Cross-functional Prompt Engineer This role focuses on shaping and owning the behavior of Claude, Anthropic's AI model, across all products. It involves authoring system prompts, developing meta-prompts for research pipelines, leading incident response for behavioral issues, and scaling best practices. The role requires strong prompting skills, technical foundations, excellent judgment, and collaboration across research, product, and safety teams. It sits at the intersection of research and product, aiming to ensure AI systems are safe, beneficial, and aligned with human values at scale. | Post-trainAgent | 9 |
| Research Engineer, Production Model Post-Training, London Research Engineer focused on post-training of production AI models, including techniques like Constitutional AI and RLHF. The role involves implementing, scaling, and optimizing these processes, conducting research to improve model quality, and developing pipelines for fine-tuning and evaluation. Requires strong software engineering skills, experience with large-scale distributed systems, and familiarity with training/fine-tuning/evaluating LLMs. The role directly impacts the quality, safety, and capabilities of production models. | Post-trainServe | 9 |
| Research Engineer, Interpretability Research Engineer focused on building and maintaining specialized infrastructure for interpretability research in AI systems. This involves developing tools for model analysis, optimizing training and inference pipelines, and ensuring reliability for safety audits, with a strong emphasis on understanding and controlling model behavior. | Post-trainServe | 9 |
| Research Engineer, Virtual Collaborator (Cowork) Research Engineer focused on training Claude for virtual collaborator workflows, involving RL environments, data creation, and evaluation systems for enterprise use cases. | Post-trainData | 9 |
| Machine Learning Systems Engineer, RL Engineering ML Systems Engineer focused on Reinforcement Learning Engineering to build, maintain, and improve the algorithms and infrastructure for training AI models like Claude using RLHF and other advanced techniques. The role emphasizes improving system performance, robustness, and usability to accelerate research breakthroughs in AI capabilities and safety. | Post-train | 9 |
| Research Engineer / Research Scientist, Biology & Life Sciences Research Engineer/Scientist role focused on applying AI/ML to accelerate progress in life sciences. The role involves developing novel evaluation frameworks and training strategies to improve AI model performance on biological research tasks, bridging domain expertise with ML engineering. It emphasizes rigorous methods, collaboration, and safety. | Post-trainEval Gate | 9 |
| Research Engineer / Scientist, Robustness Research Engineer/Scientist focused on AI robustness and safety within the Alignment Science team. The role involves conducting critical safety research and engineering to ensure AI systems can be deployed safely, with projects spanning jailbreak robustness, automated red-teaming, monitoring techniques, and applied threat modeling. It emphasizes pragmatic approaches to AI safety challenges, understanding and steering AI behavior, and contributing to research papers and safety efforts. | Post-trainAgent | 9 |
| Research Engineer, Virtual Collaborator Research Engineer focused on training Claude for virtual collaborator workflows using reinforcement learning, data pipelines, and integrating real organizational data. The role involves designing RL environments, scaling data creation, integrating enterprise data, developing evaluation systems, and training Claude on document manipulation, with a focus on enterprise AI applications. | Post-trainData | 9 |
| Research Scientist / Engineer, Agentic Learning (Horizons) Research Scientist/Engineer focused on developing and implementing novel finetuning techniques for language models to improve alignment properties like moral reasoning, honesty, and character. The role involves creating and maintaining evaluation frameworks, collaborating on production model integration, and automating scaling processes. Requires strong Python skills, ML training/experimentation experience, and analytical skills for interpreting results. Experience with language model finetuning, AI alignment research, and techniques like RLHF is preferred. | Post-train | 9 |
| Research Engineer / Scientist, Model Welfare Research Engineer/Scientist focused on understanding, evaluating, and mitigating potential welfare and moral status concerns of AI systems. This involves technical research projects on model characteristics relevant to welfare, designing interventions, and collaborating with other AI safety and alignment teams. The role also involves improving and expanding welfare assessments for frontier models and potentially deploying interventions into production. | Post-trainEval Gate | 9 |
| Research Engineer / Scientist, Alignment Science Research Engineer/Scientist focused on AI safety and alignment, conducting experiments to understand and steer AI behavior, with a focus on risks from powerful future systems. Involves collaboration with interpretability, fine-tuning, and red teaming teams. Explores scalable oversight, AI control, stress-testing, automated alignment research, alignment assessments, safeguards research, and model welfare. | Post-trainAgent | 9 |
| Machine Learning Engineer, Safeguards Research Machine Learning Engineer focused on safeguards research, bridging research and engineering. This role involves developing end-to-end pipelines and ML systems for safety research, including training/fine-tuning models, building scalable infrastructure for evaluation, implementing efficient training pipelines, and creating automated systems to understand and mitigate AI risks. The role requires strong ML fundamentals, engineering practices, and experience with Python, ML frameworks, and LLMs. | Post-trainServe | 9 |
| Research Engineer, Reward Models Research Engineer focused on developing and implementing novel reward modeling architectures and techniques to align AI systems with human values and advance AI capabilities. The role involves optimizing training and data pipelines, collaborating on integrating advances into production systems, and communicating research progress. | Post-train | 9 |
| Research Engineer, Production Model Post-Training Research Engineer focused on post-training of large language models, including techniques like Constitutional AI and RLHF, to enhance model capabilities, alignment, and safety for production Claude models. Involves implementing, scaling, and optimizing these processes, conducting research for improvements, and developing evaluation tools. | Post-train | 9 |
| Research Engineer / Scientist, Alignment Science - London Research Engineer/Scientist focused on AI safety and alignment, conducting experimental research to understand and steer the behavior of powerful AI systems. The role involves testing robustness of safety techniques, running multi-agent RL experiments, building tooling for evaluating jailbreaks, and contributing to research papers. Collaboration with Interpretability, Fine-Tuning, and Frontier Red Team is expected. | Post-trainEval Gate | 9 |
| Machine Learning Systems Engineer, RL Engineering This role focuses on building, maintaining, and improving the critical algorithms and infrastructure for training AI models, specifically using RLHF and other advanced techniques. The goal is to enhance the performance, robustness, speed, reliability, and usability of these training systems to enable breakthroughs in AI capabilities and safety. | Post-train | 9 |
| Research Manager, Production Model Training Research Manager for Anthropic's Applied Finetuning team, leading a team to train flagship production models (like Claude.AI) using techniques such as Constitutional AI and RLHF. Responsibilities include managing day-to-day execution, prioritizing work, coaching reports, and contributing technically to the team's efforts in post-training techniques, algorithm implementation, data mix experiments, evaluation design, and pipeline improvement. | Post-train | 9 |
| [Expression of Interest] Research Scientist / Engineer, Honesty Research Scientist/Engineer focused on honesty in language models, developing techniques to minimize hallucinations and enhance truthfulness. This involves data curation, classifier development, evaluation frameworks, RAG implementation, human feedback collection, prompting pipelines, RL environments, and tools for human evaluators. | Post-trainAgent | 9 |
| Model Behavior Architect, Alignment Finetuning Role focused on shaping AI system behavior for alignment with human values through prompt engineering, data generation, and rigorous testing. Involves evaluating model judgment in domains like honesty, character, and ethics, and collaborating with research teams. Requires experience in prompt engineering, AI output evaluation, and understanding of LLM training/RL concepts. | Post-trainEval Gate | 9 |
| Research Scientist/Engineer, Alignment Finetuning Research Scientist/Engineer focused on developing and implementing novel finetuning techniques to train language models for better alignment with human values (honesty, character, harmlessness). This involves using synthetic data generation, advanced training pipelines, and creating evaluation frameworks to measure alignment properties. The role also includes integrating improvements into production models and automating/scaling team workflows. | Post-train | 9 |
| Team Manager, Alignment RL Manager for a team developing and implementing AI alignment techniques, focusing on improving model values and behavior for hard-to-evaluate tasks. The role involves driving execution of alignment initiatives, supporting team growth, and ensuring collaboration across research. Key activities include implementing and scaling techniques like oversight, synthetic data generation, and training models to assist in model training, aiming to accelerate the deployment of alignment advances into frontier models. | Post-trainData | 9 |
| Research Engineer / Scientist, Safeguards Research Engineer/Scientist focused on AI safeguards, conducting critical safety research and engineering for reliable, interpretable, and steerable AI systems. The role involves testing robustness of safety techniques, running multi-agent RL experiments (AI Debate), building tooling for evaluating jailbreaks, and producing evaluation questions for model reasoning in safety-relevant contexts. It bridges research and engineering, with a focus on both immediate and long-term AI safety challenges, including risks from advanced systems and current threats. | Post-trainAgent | 9 |
| Research Engineer, Machine Learning (Horizons) Research Engineer focused on advancing LLM capabilities and safety through fundamental research in reinforcement learning, improving reasoning (code, math), and exploring RL for agentic tasks. Involves developing novel RL techniques, creating tools for models to interact with, and designing experiments. | Post-trainAgent | 9 |
| Engineering Manager, Research Tools Engineering Manager for Anthropic's Research Tools team, focusing on building and improving systems for large-scale, distributed finetuning runs and enhancing researcher productivity. The role involves prioritizing team work, designing operational processes, coaching reports, and managing recruiting efforts to support rapid growth in AI model development and research. | Post-train | 8 |
| Research Scientist, Societal Impacts Research Scientist focused on analyzing real-world usage patterns of Claude, building evaluations to assess its behavior against its Constitution (safety, quality of advice), and partnering with fine-tuning, safeguards, policy, and interpretability teams to translate insights into model improvements. The role also involves generating insights on societal impacts to inform company strategy and policy, and sharing work through publications and presentations. | Post-trainEval Gate | 8 |
| Research Scientist, Life Sciences Research Scientist role focused on applying AI to life sciences, involving both wet-lab and computational components. The role aims to establish Anthropic as a leader in biology research by developing and improving AI model performance on scientific tasks, addressing complex reasoning, and translating biological domain knowledge into ML objectives. It requires experience in life sciences R&D and familiarity with ML development practices. | Post-train | 8 |
| Anthropic Fellows Program Lead, Alignment Science This role leads and scales Anthropic's Fellows Program, a two-to-six-month initiative designed to accelerate AI safety research and foster research talent by providing funding, mentorship, and support for fellows to work on AI safety research projects. The lead will manage program operations, recruiting, research coordination, and people support, aiming to build a premier pipeline for AI safety research and talent development. | Post-train | 7 |