About the role
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 Reinforcement Learning, large-scale ML training, or a closely adjacent area Demonstrated ability to own large experiments end-to-end, from design through interpretation Proficiency in Python and experience working with large-scale or distributed ML systems Comfort operating at the research/systems boundary, including debugging where the two meet Care about the societal impacts of AI and responsible scaling Preferred
Amazon
