AI Research Scientist - Infrastructure Engineer, Reinforcement Learning

What You Do At AMD Changes Everything 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. The Role We 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 Person You 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 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 Preferred Experience 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 Prior ownership of RL training infra, LLM post-training pipelines, or large-scale experiment management Proficiency in C++/Python performance tuning, I/O optimization, and containerized workloads Academic Credentials Bachelor's degree required; Master's or PhD preferred in Computer Science for research-heavy collaboration depth is preferred. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here. This posting is for an existing vacancy.