Job Description
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 RL Scaling Science team studies how reinforcement learning behaves as we scale it (across model size, compute, and task horizon) and turns that understanding into the training recipes behind our frontier models. As a Research Engineer on this team, you'll design and run large-scale experiments to understand and resolve bottlenecks, build the benchmarks that make long-horizon progress measurable, and ship validated findings directly into production training. This role lives at the boundary between research and engineering. The problems are open, the experiments run at frontier scale, and the path from a robust result to production is short. Key responsibilities Design, run, and interpret large-scale RL experiments, reasoning rigorously about what the data does and doesn't show Investigate how RL improves as horizon, compute, and model size grow Build and maintain benchmarks for long-horizon RL so progress is measurable and reproducible Translate validated findings into production training recipes, exercising judgment about when a result is robust enough to ship Debug complex issues at the seam where research meets infrastructure - failures that only appear at scale Partner closely with adjacent RL teams across research and engineering and advance our overall RL stack Minimum qualifications Strong empirical research skills in Reinforcem
