Summer 2027 Masters AI Research Reinforcement Learning and LLM PostTraining Intern

AMD; Santa Clara (CA); Hybrid; USD 13,000 - 20,000; Full time

Benefits offered by this job

AMD Benefits at a glance

Job summary

AMD in Santa Clara, CA, is seeking a PhD candidate for an AI Research Intern specializing in RL and post-training language/code models. You will research, prototype, and evaluate RL-based methods, design experiments, and collaborate across teams to enable reproducible results.

The role emphasizes experimental rigor, publication-focused work, and exposure to cutting-edge GPU/ML workflows. Hybrid/onsite arrangements apply for the 2027 Summer internship.

Qualifications

• 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.
• Publications at leading machine-learning or computer-vision conferences are preferred.
• Exposure to GPU computing or distributed training is beneficial.

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.

Skills

Reinforcement learning knowledge

Python programming

Experiment design and analysis

Research collaboration

Education

PhD in Computer Science, ML, Electrical or Computer Engineering or related field

Tools

PyTorch

GPU computing

Job description

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/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

WHAT YOU WILL BE DOING:

We are seeking highly motivated AI Research Intern, RL and LLM Post-Training, to join our team. In this role, you will -

• 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.

WHO WE ARE LOOKING FOR:

• 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.

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.