The Lead AI Research Scientist will focus on advancing reinforcement learning techniques for post-training and interactive learning of large generative models, particularly in engineering and hardware-adjacent tasks. This role involves inventing and analyzing RL algorithms, conducting empirical studies, and collaborating with infrastructure and product teams to enhance task success while ensuring stability and safety. The ideal candidate will have a strong publication record and practical experience in RL theory and application. This position is integral to shaping the future of AI at AMD.

• PhD in Computer Science, Machine Learning, or a related field is strongly preferred.
• 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.
• 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.

• Research and develop RL methods for post-training LLMs and code models on structured engineering tasks.
• Design reward models, curricula, and training recipes suited to sparse, noisy, or expensive labels.
• Characterize failure modes and propose mitigations grounded in experiments.
• Collaborate with RL infrastructure engineers to scale training and define interfaces for rollout generation.
• Publish at top venues and contribute internal technical leadership on the RL roadmap.

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