Summer 2027 PhD Gen AI and Reinforcement Learning Research Intern

Advanced Micro Devices, Inc•Santa Clara, CA

1d•Hybrid

About The Position

As an AMD intern, you’ll be placed at the epicenter of the AI ecosystem, working alongside experts and industry pioneers. You’ll do important work, learn new skills, expand your network, and gain real-world experience on projects that impact millions of end-users worldwide. Whether you’re an undergrad or a PhD student, your contributions matter—and your experience here will be a launchpad for what comes next.

Requirements

Must be currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Electrical or Computer Engineering, or a related field.
Strong knowledge of reinforcement learning and modern deep-learning methods.
Experience implementing and evaluating machine-learning models using Python and PyTorch.
Familiarity with LLM post-training, RLHF/RLAIF, preference optimization, or language and multimodal agents.
Experience conducting reproducible experiments and analyzing empirical results.

Nice To Haves

Publications at leading machine-learning or computer-vision conferences—such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV—are preferred.
Experience with large-scale model training, GPU computing, or distributed systems is beneficial.
Familiarity with generative AI, multimodal learning, reasoning models, code models, or agentic systems is a plus.

Responsibilities

Research and prototype methods for post-training large generative models.
Explore policy optimization, preference learning, reward modeling, exploration, and credit-assignment techniques.
Develop methods for improving reasoning, code generation, tool use, and agentic behavior.
Design and run controlled experiments using verifiable, preference-based, or model-generated feedback.
Analyze failure modes such as reward hacking, policy degeneration, and training instability.
Develop evaluations that measure model quality, robustness, safety, and real-world task performance.
Collaborate with research and infrastructure teams on training, rollout generation, logging, and reproducibility.
Document findings and contribute to technical reports and publications.