Research Engineer, RL Scaling Science

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The 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. This role sits at the boundary between research and engineering: problems are open, experiments run at frontier scale, and the path from a robust result to production is short.

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 the overall RL stack

Requirements

• 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
• Minimum education: Bachelor’s degree or equivalent combination of education/training/experience
• Minimum years of experience: will correlate with internal job level requirements

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

Logistics & Additional Info

• Location: London, UK (location-based hybrid policy: expected to be in an office at least 25% of the time)
• Annual salary range: £375,000 - £640,000 GBP
• Visa sponsorship: Anthropic states they sponsor visas and retain an immigration lawyer to assist, though not every role/candidate can be guaranteed
• Guidance on candidate AI usage and other application instructions are provided on Anthropic's careers pages

How We're Different

Anthropic treats AI research as a big-science empirical effort, working as cohesive teams on a few large-scale research efforts. The organization emphasizes impact, collaboration, communication skills, and consideration of AI's social and ethical implications.