Build and improve the RL training infrastructure used daily by researchers
Identify and remove bottlenecks across the RL stack (debugging, profiling)
Partner with researchers and adjacent engineering teams to ship tooling that makes them faster
Own the reliability and performance of RL research runs end-to-end
Contribute to design decisions shaping RL at scale

🎯 Requirements

Strong software engineering fundamentals and reliable, performant systems
Experience in ML infrastructure, distributed systems, or research tooling
Enable others' work via platforms rather than individual experiments
Comfortable across the stack—from low-level perf to RL algorithms
Bias toward shipping and iterating quickly with high agency and low ego

🎁 Benefits

Competitive compensation and benefits
Optional equity donation matching
Generous vacation and parental leave
Flexible working hours
Office space for collaboration in San Francisco

🛃 Visa sponsorship

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