Summer 2027 Master's AI Research, Reinforcement Learning and LLM Post-Training Intern

About the role

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.

Responsibilities

• Research and prototype RL methods for post‑training language and code models.
• Explore policy optimization, preference learning, reward modeling, exploration, and credit‑assignment techniques.
• Design and run controlled experiments using verifiable, preference‑based, or simulator‑generated feedback.
• Analyze failure modes such as reward hacking, policy degeneration, and training instability.
• Develop evaluation methods that reflect realistic engineering constraints.
• Collaborate with research and infrastructure teams on rollout generation, training, logging, and reproducibility.
• Document findings and contribute to technical reports and publications.

Requirements

Must be currently pursuing a PhD in Computer Science, Machine Learning, Electrical or Computer Engineering, or a related field.

• Knowledge of reinforcement learning and modern deep‑learning methods.
• Experience implementing and evaluating machine‑learning models using Python and frameworks such as PyTorch.
• Familiarity with LLM post‑training, RLHF/RLAIF, preference optimization, or language and code agents.
• Experience conducting reproducible experiments and analyzing empirical results.

Publications at leading machine‑learning or computer‑vision conferences—such as ICML, NeurIPS, ICLR, CVPR, ICCV, or ECCV—are preferred.

Exposure to GPU computing or distributed training is beneficial.

Familiarity with compilers, kernels, optimization, EDA workflows, or large‑scale codebases is a plus.

Schedule

Location: Santa Clara, CA, USA

Onsite/Hybrid: This role requires the student to work full time (40 hours a week), in either a hybrid or onsite work structure throughout the duration of the co‑op/intern term.

Duration: Summer 2027 Internship

Semester Schools: May 24, 2027 – August 13, 2027

Quarter Schools: June 21, 2027 – September 10, 2027