AI Research Scientist - Infrastructure Engineer, Reinforcement Learning

What You Do At AMD Changes EverythingAt 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.The RoleWe are hiring an AI Research Scientist - 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.The PersonYou profile before you optimize; you treat researcher time as expensive as GPU time. You communicate SLAs, capacity plans, and incident patterns clearly and partner on cost–quality tradeoffs.Key ResponsibilitiesDesign and implement distributed RL training stacks (data parallel, pipeline parallel, or hybrid) integrated with AMD's schedulers and storageBuild high-throughput rollout workers, trajectory stores, and reward computation pipelines with versioning and audit trailsInstrument jobs for debugging (NaNs, stragglers, OOMs), implement autoscaling and preemption-safe checkpointingCollaborate with research scientists on experiment templates, hyperparameter sweeps, and safe promotion paths from research to wider team useDrive reliability: on-call rotations, runbooks, and postmortems for infra incidents affecting RL trainingPreferred ExperienceStrong 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 orchestrationPrior ownership of RL training infra, LLM post-training pipelines, or large-scale experiment managementProficiency in C++/Python performance tuning, I/O optimization, and containerized workloadsAcademic CredentialsBachelor's degree required; Master's or PhD preferred in Computer Science for research-heavy collaboration depth is preferred.