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Research Engineer, RL Scaling Science

Anthropic · 🇬🇧 London, United Kingdom

Sponsorship Verification Proof

✓ 🇬🇧 UK Home Office Licensed Sponsor

Employer is on the UK Home Office register of licensed sponsors.

UK Home Office Register of Licensed Sponsors (Skilled Worker route): listed as 'Anthropic Limited', London — Skilled Worker; Worker (A rating)

An official government register match confirms the employer holds an active sponsor licence in this country. It does not guarantee that every individual role meets minimum salary or skill thresholds — always confirm sponsorship eligibility with the recruiter.

The job posting also states: “Visa sponsorship: We do sponsor visas!”

Verification category
Employer on official sponsor register
Role category
Backend
Last verified
2026-10-25
First indexed
2026-10-25
Posted date
2026-06-22
ATS / Feed source
Greenhouse job board

Role Description (Excerpt)

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…

Excerpt only — read the full requirements and apply directly on Anthropic's career page. We do not process applications.