PhD AI Research Infrastructure, RL Post-Training Intern

Amd

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Santa Clara, CA
Salary
$91,520–$137,280 / yr
Employment
Intern
Posted
10 days ago
Freshness
Confirmed live today
Closes
Sep 21, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $184k
This role $114k
$73k most similar roles pay here $265k

This role pays less than 75% of similar roles. Most pay $121,627–$246,000 — the shaded band above. At the midpoint, this role pays about $114k versus about $184k 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 · PhD AI Research Infrastructure, RL Post-Training Intern

The Summer 2027 PhD AI Research Infrastructure, RL Post-Training Intern will join the research team to develop and optimize infrastructure for the post-training of large language and multimodal models. The role involves building scalable systems for rollout generation, inference, reward computation, and policy updates while improving distributed training efficiency, reliability, and resource utilization. The intern will design interfaces for researchers to evaluate new algorithms, build tools for experiment configuration and monitoring, and profile end-to-end pipelines to resolve performance bottlenecks. Required skills include Python, PyTorch, and expertise in reinforcement learning, RLHF/RLAIF, or preference optimization. Candidates should possess experience with multi-GPU workloads, parallelism strategies like tensor or pipeline parallelism, and distributed communication. The work focuses on creating production-quality infrastructure for large-scale inference and training workflows within the AI research ecosystem.

What you'll do

  • Develop and optimize infrastructure for RL-based post-training of large language and multimodal models.
  • Build scalable systems for rollout generation, inference, reward computation, and policy updates.
  • Improve distributed training efficiency, reliability, fault tolerance, and resource utilization.
  • Design interfaces that enable researchers to implement and evaluate new RL algorithms quickly.
  • Create tools for experiment configuration, checkpointing, logging, monitoring, and reproducibility.
  • Profile end-to-end training pipelines to resolve performance, memory, and communication bottlenecks.
  • Support on-policy and off-policy training workflows using verifiable or model-generated feedback.
  • Document system designs and contribute to technical reports and publications.

What we're looking for

  • Must be currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, or a related field.
  • Strong programming skills in Python and experience with PyTorch are required.
  • Knowledge of reinforcement learning, LLM post-training, RLHF/RLAIF, or preference optimization is required.
  • Experience with distributed training, multi-GPU workloads, or large-scale inference is required.
  • Familiarity with parallelism strategies such as data, tensor, pipeline, or sequence parallelism is required.
  • Experience with training and inference frameworks, orchestration systems, or cluster environments is required.
  • Understanding of GPU performance, memory management, networking, and distributed communication is required.
  • Experience building research infrastructure, profiling systems, or debugging distributed workloads is required.
  • Familiarity with containerization, experiment tracking, and cloud or cluster computing is beneficial (preferred).
  • Publications at leading venues such as ICML, NeurIPS, ICLR, MLSys, CVPR, ICCV, or ECCV are preferred.

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