Research Engineer, RL Scaling Science

Responsibilities

• Design, run, and interpret large-scale reinforcement learning experiments, reasoning rigorously about what the data does and does not show.
• Investigate how reinforcement learning improves as horizon, compute, and model size grow.
• Build and maintain benchmarks for long-horizon reinforcement learning 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 between research and infrastructure, including failures that appear only at scale.
• Partner closely with adjacent reinforcement learning teams across research and engineering and advance the overall reinforcement learning stack.

Requirements

• Strong empirical research skills in reinforcement learning, large-scale machine learning 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 machine learning systems.
• Comfort operating at the research and systems boundary, including debugging where the two meet.
• Care about the societal impacts of artificial intelligence and responsible scaling.
• Bachelor's degree or an equivalent combination of education, training, and experience in a relevant field, as demonstrated through coursework, training, or professional experience.

Nice-to-haves

• Published or shipped work in long-horizon reinforcement learning or reinforcement learning fundamentals.
• Experience translating research findings into production training recipes.
• Demonstrated large-scale industry impact through reinforcement learning interventions.
• Experience working on frontier-scale training runs with long trajectories.

Representative Projects

• Design a benchmark suite for long-horizon reinforcement learning 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 a reinforcement learning run and trace it to a root cause spanning algorithm, data, and infrastructure.

Compensation and Benefits

• Annual salary: £375,000–£640,000 GBP.
• Hybrid policy: staff are expected to work from an office at least 25% of the time; some roles may require more office time.
• Visa sponsorship is available for eligible roles and candidates.
• Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.