AI Research Scientist, Reinforcement Learning (LLM) and Post-Training

Key Responsibilities

• Research and develop RL methods for post-training LLMs and code models on structured engineering tasks with verifiable or preference-based feedback
• Design reward models, curricula, and off-policy or on-policy training recipes suited to sparse, noisy, or expensive labels from experts and simulators
• Characterize failure modes (reward hacking, degenerate policies, instability) and propose mitigations grounded in experiments
• Collaborate with RL infra engineers to scale training; define interfaces for rollout generation, logging, and reproducibility

Preferred Experience

• Strong publication record in reinforcement learning or closely related machine learning areas
• Hands-on experience training RL or preference-optimized models at non-trivial scale (GPUs, distributed jobs)
• Experience with LLM post-training, RLHF/RLAIF, or policy optimization for language or code agents
• Familiarity with compilers, kernels, EDA-style workflows, or large-scale codebases is a plus

Benefits

Benefits offered are described: AMD benefits at a glance.