Join Anthropic as a Research Engineer to design and run large-scale experiments in Reinforcement Learning for AI systems.
Posted by employer 3 months ago
First seen on Joblaze 3 months ago
Last verified on the company career page 8 hours ago
Skills & Technologies
Requirements
- Education
- Bachelor's degree
- Visa
- Sponsorship available
Not disclosed in this posting: years of experience.
Benefits
Joblaze summary
In this role, the Research Engineer on the RL Scaling Science team at Anthropic focuses on designing and executing large-scale experiments to enhance reinforcement learning systems. Key skills include strong empirical research capabilities, proficiency in Python, and experience with distributed machine learning systems. This position is ideal for individuals with a solid background in reinforcement learning and a passion for translating research into practical applications. Anthropic emphasizes collaboration and aims to make a significant impact in the AI field through its research efforts.
Joblaze insights
- Listed about 3 months ago — first seen on Joblaze June 23, 2026. Last confirmed on Anthropic's careers page October 9, 2026.
- Starts above 100% of 11 comparable mid ai/ml roles in United Kingdom we track (median $79,313 across 7 companies).
- Python appears in 44.2% of 52 comparable mid ai/ml roles in United Kingdom; reinforcement learning appears in 1.9% of 52 comparable mid ai/ml roles in United Kingdom.
Quick facts
- Is the Research Engineer, RL Scaling Science role remote?
- It's hybrid — Anthropic expects some on-site time in London, UK.
- What's the salary range?
- Anthropic lists £375,000–£640,000 for this role.
- Where is the role based?
- Anthropic is hiring for this position in London, UK.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Machine Learning, Python, reinforcement learning.
- Does Anthropic sponsor work visas for this role?
- Yes — the posting indicates visa sponsorship is available for the right candidate.
- What seniority level is this role?
- Anthropic targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Engineer, RL Scaling Science role at Anthropic.
From the original posting
About Anthropic
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.
Logistics
Standard company text repeated across Anthropic's postings is omitted here.