Research Engineer - RL Infrastructure

Highlights

• Build infrastructure powering large-scale RL and frontier model training.
• Optimize end-to-end training efficiency across compute and networking.
• Shape architecture of async rollout and post-training systems.

What they offer

• Equity incentives
• Flexible work arrangements with remote or in-person option
• Visa sponsorship and relocation assistance
• Quarterly team off-sites, hackathons, conferences and learning opportunities

Ideal candidate

Systems-minded engineer who thrives on ambiguous, high-ownership problems and loves squeezing performance from large-scale training. Motivated by open frontier AI and turning research bottlenecks into real infrastructure.

Requirements

• Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference
• Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray or related tooling
• Experience optimizing training performance across kernels, memory movement, communication overhead or parallelization strategy
• Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism and pipeline parallelism
• Strong understanding of GPU architecture, profiling and performance debugging
• Ability to identify bottlenecks across the stack and drive improvements from first principles
• Comfort working in a fast-moving environment with ambiguous problems and high ownership