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 qualifications
Published or shipped work in long-horizon RL or RL fundamentals
Experience translating research findings into production training recipes
Demonstrated large scale industry impact via RL interventions
Experience working on frontier-scale training runs with long trajectories
Representative projects
Design a benchmark suite for long-horizon RL that distinguishes genuine capability gains from artifacts of evaluation setup
Take a promising experimental finding, stress-test it across model scales, and work with training teams to land it in a production recipe
Investigate an unexpected scaling trend in an RL run and trace it to a root cause spanning algorithm, data, and infrastructure
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
£375,000—£640,000 GBP
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demo
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