About the team The Tool Use team within Research is responsible for making Claude the world's most capable, reliable, safe, and efficient model for tool use and agentic applications. The team focuses on the foundational layer - solving core problems such as tool call accuracy, long horizon & complex tool use workflow, large scale & dynamic tools, tool hallucination, tool use safety (e.g. prompt injection robustness), and tool use efficiency. These are foundations to the majority of Anthropic’s customers as well as internal teams building specific agentic capabilities such as coding (including Claude Code), search & deep research, memory, and multi-agents. About the role We're looking for Research Engineers/Scientists to help us advance the frontier of tool use research. Today's AI agents handle impressive workflows. With tool use adoption accelerating rapidly across our platform, the next generation requires even more breakthrough research: for example, enabling models to reliably orchestrate vast tool ecosystems, maintain safety in autonomous operations, and scale to handle the increasing complexity of real-world tasks. You'll collaborate with a diverse group of researchers and engineers to advance Claude's tool use capabilities and safety. You'll own the full research lifecycle—from identifying fundamental limitations to implementing solutions that ship in production models. We value diverse perspectives and exceptional depth in specific areas—whether that's groundbreaking RL research, exceptional engineering, or novel approaches from other quantitative fields. Responsibilities: Define and pursue research agendas that push the boundaries of what's possible Design and implement novel reinforcement learning environments and methodologies that push the state of the art of tool use Build rigorous, realistic evaluations that capture the complexity of real-world tool use Ship research advances that directly impact millions of users Collaborate with other frontier research and product teams to drive fundamental breakthroughs in capabilities and safety, and work with teams to ship these into production Design, implement, and debug code across our research and production ML stacks Contribute to our collaborative research culture through pair programming, technical discussions, and team problem-solving You may be a good fit if you: Are driven by real-world impact and excited to see research ship in production Have strong machine learning research/applied-research experience, or a strong quantitative background such as physics, mathematics, or quant research Write clean, reliable code and have solid software engineering skills Communicate complex ideas clearly to diverse audiences Are passionate about building AI systems that are both powerful and safe Are hungry to learn and grow, regardless of years of experience Strong candidates may also have one or more of the following: Experience with reinforcement learning techniques and environments Experience with language model training, fine-tuning or evaluation Experience building AI agents or autonomous systems Published influential work in relevant ML areas Deep expertise in a specific area (e.g., exceptional RL research, systems engineering, or mathematical foundations) even if still developing in other areas Experience shipping features or working closely with product teams Enthusiasm for pair programming and collaborative research

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