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

Amd

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Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CA
Salary: $83,200–$124,800 / yr
Employment: Intern
Posted: 10 days ago
Freshness: Confirmed live today
Closes: Sep 21, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $207k

This role $104k

$62k most similar roles pay here $284k

This role pays less than 93% of similar roles. Most pay $162,000–$252,237 — the shaded band above. At the midpoint, this role pays about $104k versus about $207k for comparable roles.

Based on 240 similar postings.

Employer

About Amd

AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors

Amd currently has 474 open roles on FindRole.

Listed pay typically runs $166,400–$249,600 across 474 roles with salary data.

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At a glance

TL;DR · Master's AI Research, Reinforcement Learning and LLM Post-Training Intern

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The Summer 2027 Master's AI Research, Reinforcement Learning and LLM Post-Training Intern will join the research team to focus on post-training language and code models. The role involves researching and prototyping reinforcement learning methods, exploring policy optimization, preference learning, reward modeling, exploration, and credit-assignment techniques. You will design controlled experiments using verifiable or simulator-generated feedback while analyzing failure modes like reward hacking and training instability. Collaborating with infrastructure teams, you will work on rollout generation, training, logging, and reproducibility to produce technical reports and publications. The position requires expertise in Python and PyTorch, along with knowledge of RLHF, RLAIF, and preference optimization. Candidates should have experience in machine learning models, reproducible experiments, and potentially GPU computing or distributed training while addressing the specific challenges of large-scale language and code agent development.

Skills

Reinforcement Learning LLM Post-Training RLHF RLAIF PyTorch Python deep-learning GPU Computing Distributed Training Reward Modeling Preference Optimization Machine Learning computer-vision Compiler EDA Workflows

What you'll do

Research and prototype RL methods for post-training language and code models.
Explore policy optimization, preference learning, reward modeling, and credit-assignment techniques.
Design and run controlled experiments using verifiable 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, and logging.
Document findings and contribute to technical reports and publications.

What we're looking for

Must be currently pursuing a PhD in Computer Science, Machine Learning, Electrical Engineering, 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 are preferred (preferred).
Exposure to GPU computing or distributed training is beneficial (preferred).
Familiarity with compilers, kernels, optimization, EDA workflows, or large-scale codebases is a plus (preferred).