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, 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 |
| 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-train |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Technical Curriculum Engineering Lead This role focuses on creating technical educational content and demos for Anthropic's AI models, translating complex AI concepts into practical learning experiences for developers. It involves partnering with research and product teams to showcase Claude's capabilities and influence user education strategy. | Post-train | 7 |