Research Engineer - RL Infrastructure

Highlights

• Build systems infrastructure for large-scale RL and distributed training.
• Shape architecture of async rollout and post-training RL stack at frontier scale.
• Optimize end-to-end training efficiency across compute, memory and networking.

What they offer

• Equity
• Flexible work arrangements with remote or in-person option
• Visa sponsorship and relocation support
• Quarterly team offsites, hackathons and conferences
• Learning opportunities

Ideal candidate

Systems-minded engineer who loves squeezing performance from large-scale training and thrives on ambiguous, high-ownership infrastructure problems alongside researchers.

Requirements

• Strong systems engineering experience in AI/ML infrastructure for large-scale training or inference
• Deep familiarity with PyTorch and distributed training frameworks
• Experience optimizing training performance across kernels, memory, communication or parallelization
• Hands-on experience with data, tensor and pipeline parallelism
• Strong understanding of GPU architecture, profiling and performance debugging
• Ability to identify bottlenecks and drive improvements from first principles
• Comfort working with ambiguity and high ownership in fast-moving environment