RL Infrastructure Engineer

The role combines systems engineering with a practical understanding of RL. Infrastructure problems can change learning behavior: delayed weight updates, differences between trainer and inference log probabilities, or off-policy drift can resemble problems with the training method itself. You’ll work closely with researchers to understand these interactions and improve performance without compromising training correctness., * Training and rollout systems. Coordinate trainers, rollout workers, environment execution, and reward computation across GPU clusters. Integrate SGLang for high-throughput sampling, improve trainer-to-inference weight synchronization, and support asynchronous rollouts and off-policy training.

• Performance at scale. Improve distributed training through FSDP, tensor and pipeline parallelism, and efficient NCCL communication. Profile bottlenecks across compute, memory, and networking, and make targeted runtime or CUDA/Triton kernel improvements where they meaningfully improve training efficiency.
• Training correctness and reliability. Develop monitoring and diagnostics that catch stale weights, trainer-inference log-probability mismatches, and off-policy drift during long runs. Build reproducible debugging workflows and recovery mechanisms, and use controlled comparisons to understand how infrastructure changes affect learning.

• Strong Python and PyTorch skills, with experience building distributed training or inference infrastructure.
• Practical understanding of LLM reinforcement learning, including rollout policies, reward computation, and policy updates.
• Experience profiling GPU workloads and resolving performance, correctness, or reliability issues in complex ML systems.
• Ability to investigate problems across research and infrastructure code and validate changes on real training runs.
• Experience developing RL training pipelines, particularly asynchronous or off-policy systems.
• Work on agent environments, sandboxed execution, reward models, or verification pipelines.
• Experience extending SGLang, vLLM, or comparable inference runtimes.
• Experience with FSDP, tensor or pipeline parallelism, NCCL, CUDA, or Triton.