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

Nice to Haves

• 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

What You'll Be Doing

• 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

Perks and Benefits

• Generous vacation and parental leave
• Flexible working hours
• Competitive compensation and benefits
• Optional equity donation matching
• Lovely office space in which to collaborate with colleagues