The AI Research Scientist role at AMD focuses on advancing reinforcement learning techniques for post-training and interactive learning in large generative models. The position 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. Candidates should be proficient in both RL theory and practical applications, with a strong emphasis on real-world constraints. This role is integral to shaping the future of technology 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, including GPUs and 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.

• 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 such as reward hacking, degenerate policies, and instability, and propose mitigations grounded in experiments.
• Collaborate with RL infrastructure engineers to scale training and define interfaces for rollout generation, logging, and reproducibility.
• Publish at top venues like NeurIPS, ICML, and ICLR, 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.