ADVANCE YOUR CAREER. ADVANCE THE WORLD. 

At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future. 

 

Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.

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.

 

JOB DETAILS:

• Location: Santa Clara, CA, USA
• Onsite: This role requires the student to work full time (40 hours a week), 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

WHAT YOU WILL BE DOING:

 

We are seeking highly motivated AI Research intern to join our team. In this role –

• 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.

WHO WE ARE LOOKING FOR:

• 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.
• 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.

This role is not eligible for visa sponsorship. 

Benefits offered are described:  AMD benefits at a glance.

 

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

 

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available here.

 

This posting is for an existing vacancy.