The Lead AI Infrastructure Engineer, Reinforcement Learning at AMD will be responsible for managing reinforcement learning infrastructure at scale. This includes overseeing distributed policy and value training, rollout generation, and researcher-facing APIs across large GPU fleets. The role emphasizes improving the productivity of RL scientists by enhancing system reliability and performance. The ideal candidate will work closely with research scientists to ensure efficient and effective infrastructure solutions.

• Bachelor's degree required; Master's or PhD preferred in Computer Science.
• Strong systems track record in machine learning platforms.
• Deep experience with PyTorch or JAX, NCCL/MPI-style distributed training, and GPU cluster orchestration.
• Prior ownership of RL training infrastructure or large-scale experiment management.
• Proficiency in C++ and Python performance tuning, I/O optimization, and containerized workloads.

• Design and implement distributed RL training stacks integrated with AMD's schedulers and storage.
• Build high-throughput rollout workers, trajectory stores, and reward computation pipelines.
• Instrument jobs for debugging and implement autoscaling and preemption-safe checkpointing.
• Collaborate with research scientists on experiment templates and hyperparameter sweeps.
• Drive reliability through on-call rotations, runbooks, and postmortems for infrastructure incidents.

• AMD provides a competitive 'Total Rewards' package that focuses on financial growth, health, and work-life balance.