Lead AI Infrastructure Engineer, Reinforcement Learning
About The Position
At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. We are hiring a Lead AI Infrastructure Engineer, Reinforcement Learning, to own reinforcement learning infrastructure at scale—including distributed policy and value training, rollout generation, logging, checkpointing, and researcher‑facing APIs across large GPU fleets. You make RL scientists productive by improving throughput, fault tolerance, reproducibility, and observability—turning fragile notebooks into reliable systems that RSI, generalizing-HW, and RL research programs depend on.
Requirements
- Strong systems track record in machine learning (ML) platforms with deep systems expertise and demonstrated technical impact.
- Deep experience with PyTorch (or JAX), NCCL/MPI-style distributed training, and GPU cluster orchestration
- Proficiency in C++/Python performance tuning, I/O optimization, and containerized workloads
Nice To Haves
- Prior ownership of RL training infra, LLM post-training pipelines, or large-scale experiment management
Responsibilities
- Design and implement distributed RL training stacks (data parallel, pipeline parallel, or hybrid) integrated with AMD’s schedulers and storage
- Build high-throughput rollout workers, trajectory stores, and reward computation pipelines with versioning and audit trails
- Instrument jobs for debugging (NaNs, stragglers, OOMs), implement autoscaling and preemption-safe checkpointing
- Collaborate with research scientists on experiment templates, hyperparameter sweeps, and safe promotion paths from research to wider team use
- Drive reliability: on-call rotations, runbooks, and postmortems for infra incidents affecting RL training
Benefits
- AMD benefits at a glance.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
