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 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 |
| Research Engineer / Research Scientist, Tokens Research Engineer/Scientist role focused on building large-scale ML systems, touching all parts of code and infrastructure, from cluster reliability and job efficiency to running scientific experiments and improving dev tooling. The role involves optimizing ML systems, comparing model variants, scaling training jobs, and designing fault tolerance strategies, with a focus on safe, steerable, and trustworthy AI. | Pretrain |
| 9 |
| ML/Research Engineer, Safeguards ML/Research Engineer focused on detecting and mitigating misuse of AI systems, building classifiers, monitoring for harms, evaluating agentic product safety, and conducting research on red-teaming and adversarial robustness. | AgentData | 9 |
| Research Operations & Strategy Lead - Coding & Cybersecurity Data This role focuses on building and scaling data operations for AI models, specifically for coding and cybersecurity capabilities. The lead will partner with research teams to design and execute data strategies, manage vendors, and oversee the data pipeline from requirements to production. While not hands-on engineering, technical depth in understanding training data quality is required, with a focus on strategy and execution. | DataAgent | 9 |
| Privacy Research Engineer, Safeguards Research Engineer focused on privacy for large language models, developing and auditing privacy-preserving training algorithms and techniques, and ensuring responsible data handling. | DataPost-train | 9 |
| Research Engineer, Pretraining Scaling - London Research Engineer focused on pretraining and scaling large language models, involving performance optimization, debugging, experimental design, and ensuring reliability of production training pipelines. The role is highly operational, requiring on-call incident response during model launches, and involves building and maintaining training infrastructure and codebase capabilities. | Pretrain | 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, Tool Use Safety Research Engineer/Scientist focused on advancing the frontier of safe tool use in AI models, specifically addressing prompt injection, data exfiltration, adversarial attacks, and autonomous agent behavior with large tool sets. The role involves designing and implementing RL methodologies, building evaluations, and shipping research advances into production models, with a strong emphasis on safety and reliability. | AgentPost-train | 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 / Scientist, Tool Use Research Engineer/Scientist focused on advancing the frontier of tool use for AI agents, aiming to improve accuracy, reliability, safety, and efficiency in complex workflows. The role involves defining research agendas, designing RL methodologies, building evaluations, and shipping research advances into production models, with a strong emphasis on safety and collaboration. | AgentPost-train | 9 |
| Research Engineer, Model Performance & Quality Research Engineer focused on systematically understanding and monitoring model quality in real-time. This role involves training production models, developing monitoring systems, and creating novel evaluation methodologies, bridging research and production across the model training pipeline. | Eval GatePost-train | 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, Model Performance & Quality Research Engineer focused on systematically understanding and monitoring model quality in real-time. This role involves training production models, developing monitoring systems, and creating novel evaluation methodologies, bridging research and production across the model training pipeline. | Eval GatePost-train | 9 |
| Research Manager, Tokens Research Manager for the Pretraining Data team (Tokens) at Anthropic. Focuses on understanding and innovating pretraining data for foundational AI models, including data trends, scaling laws, data sources, and processing methodologies. Leads a team of researchers and engineers. | Pretrain | 9 |
| Research Scientist / Research Engineer, Pre-training Research Engineer role focused on the pre-training of large language models, involving research into model architecture, algorithms, data processing, and optimizer development, as well as scaling training infrastructure and developing dev tooling. Requires advanced degree, strong software engineering skills, and familiarity with large-scale ML and deep learning frameworks. | Pretrain | 9 |
| Research Engineer, Discovery Research Engineer focused on building and optimizing infrastructure for AI scientist training, evaluation, and inference. The role involves identifying and resolving infra blockers, developing evaluation frameworks, managing data pipelines, and optimizing training/inference for reinforcement learning in distributed environments. | ServeData | 9 |
| Research Engineer, Tokens ML Infra Research Engineer focused on ML training infrastructure for large language models, involving JAX/PyTorch, distributed systems, performance optimization, and MLOps tooling to support novel training architectures and experimentation. | Pretrain | 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 |
| Research Engineer / Research Scientist, Multimodal Research Engineer/Scientist focused on building and studying multimodal AI systems, including training, inference, system design, and data collection. The role involves developing new architectures, reinforcement learning environments, high-performance serving infrastructure, and data processing tools for multimodal data. | PretrainPost-train | 9 |
| Research Scientist, Tokens (Multimodal) Research Scientist focused on multimodal AI systems, working on training, inference, system design, and data collection. The role involves developing new architectures for multimodal data, building infrastructure for RL environments and RPC servers, and collecting/processing large-scale multimodal data. Emphasis on foundational research and large-scale experiments. | PretrainPost-train | 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 |
| Research Engineer, Knowledge Team Research Engineer focused on redesigning how Claude interacts with external data sources by designing new information architectures and training language models to use them. This includes performing finetuning and RL, building knowledge base eval sets, and designing/evaluating agentic search capabilities. | AgentPost-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, Societal Impacts Research Scientist focused on empirical studies of AI's societal impacts, developing measurement systems and evaluation frameworks, and translating insights into product/policy recommendations. This role involves both quantitative and qualitative methods, with a focus on areas like economics, well-being, education, and alignment. | Eval GateAgent | 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 |
| Research Engineer, Tokens (Pre-training) Research Engineer focused on pretraining data for large-scale AI models. Responsibilities include understanding data trends, scaling laws, optimizing data mixes, exploring new data sources, building research tools for analysis, and effective data processing. Strong software engineering and empirical research skills are required. | Pretrain | 9 |
| Research Scientist, Frontier Red Team (CBRN, Biosecurity) Research Scientist focused on red-teaming AI models for biosecurity risks, involving fine-tuning, threat modeling, and developing novel evaluations. This role bridges AI safety research with domain expertise in biosecurity. | Eval GatePost-train | 9 |
| Research Scientist, Frontier Red Team (Autonomy) Research Scientist role focused on developing and productionizing advanced autonomy evaluations for AI Safety Level (ASL) determination of models. This involves risk and capability modeling, designing, implementing, and running large-scale experiments to evaluate autonomous capabilities and forecast future capabilities, with potential for people management. | Eval GateAgent | 9 |
| Research Engineer, Frontier Red Team (RSP Evaluations) Research Engineer focused on developing and running "gold standard" evaluations for catastrophic risks to ensure safe release of frontier AI models, aligning with the Responsible Scaling Policy (RSP). The role involves creating evaluation systems, collaborating with domain experts, building sandboxed testing environments, and informing critical deployment decisions. | Eval Gate | 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, Societal Impacts Research Engineer focused on building infrastructure for foundational research into AI's societal impact. This involves designing and implementing scalable systems for experiments, evaluations, and data processing, with a strong emphasis on reliability and supporting future research directions. The role requires close collaboration with researchers and policy experts to generate insights and inform strategy. | Eval Gate | 9 |
| Research Manager, Horizons Research Manager for the Horizons team at Anthropic, focusing on RL with LLMs, code generation, reasoning, tool use, and agents. The role involves team management, project planning, vision-setting, people development, and ensuring execution aligns with AI safety goals. | Pretrain | 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 |
| Research Engineer, Knowledge Team Research Engineer focused on redesigning how LLMs interact with external data sources by designing new information architectures and training models to use them. Responsibilities include implementing information architecture strategies, performing finetuning and RL, building knowledge base eval sets, and designing agentic search capabilities. Requires strong Python, ML research experience, and experience with LLMs. Experience with complex agentic systems, RAG, and distributed information retrieval is a plus. | AgentPost-train | 9 |
| Research Engineer, Agents Research Engineer focused on advancing agentic AI systems, involving finetuning Claude for agentic tasks, developing tools for agents (memory, communication), prompt engineering, automated evaluation, and optimizing data mixes for model training. The role also involves creating and maintaining infrastructure for prompt iteration and testing. | AgentPost-train | 9 |
| Data Operations Manager, Human Data This role focuses on building and scaling data operations for research teams working on frontier AI capabilities, including RLHF, safety, tool use, and agentic workflows. The Data Operations Manager will own data strategy, manage vendor partnerships, and implement systems to ensure high-quality training data, directly impacting model performance on critical capabilities. | DataPost-train | 8 |
| Anthropic Fellows Program — ML Systems & Performance This is a research fellowship program focused on AI systems and performance, with the goal of producing public outputs like paper submissions. Fellows will work on empirical projects, potentially involving building ML systems, data pipelines, or infrastructure for accelerators, using external infrastructure and open-source models. | Data | 8 |
| Research Engineer / Scientist, Societal Impacts Research Engineer/Scientist focused on designing and building infrastructure to study the societal impacts of AI systems, enabling research, policy, and product improvements. This role involves creating scalable systems for experiments, analysis, and monitoring, with a strong emphasis on collaboration and shipping impactful changes. | ShipEval Gate | 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 |
| Biological Safety Research Scientist Research Scientist focused on biological safety for AI systems, applying technical skills to design and develop safety systems that detect harmful behaviors and prevent misuse. This role involves designing and executing capability evaluations, collaborating on training data and safety system training, analyzing performance, and stress-testing safeguards. The goal is to ensure biological safety is embedded throughout the model development lifecycle, balancing AI's potential in life sciences with preventing misuse. | Eval GatePost-train | 8 |
| Research Engineer, Data Ingestion Research Engineer role focused on building and scaling a large-scale web crawler for data ingestion, with a focus on data quality evaluation and improvement to support the creation of pretrained models. | Data | 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 |
| Research Engineer, Societal Impacts Research Engineer focused on building infrastructure for studying the societal impacts of AI systems, including economic, wellbeing, and educational effects, as well as socio-technical alignment and novel capability evaluation. The role involves designing and implementing scalable technical infrastructure for experiments, data pipelines, and monitoring systems, working closely with researchers and cross-functional partners to generate insights and advance AI safety. | AgentEval Gate | 8 |
| Data Scientist, Safeguards This role focuses on building and scaling a data-driven culture within an AI company, specifically for safeguards. The Data Scientist will analyze user behavior, define key metrics, identify opportunities for product improvement, design and analyze experiments, and establish data best practices to inform product and commercial strategy for safe, frontier AI deployment. | Eval Gate | 7 |