Summer 2027 PhD AI Research Infrastructure, RL Post-Training Intern

Advanced Micro Devices, Inc•Santa Clara, CA

1d•Hybrid

About The Position

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.

Requirements

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.
Knowledge of reinforcement learning, LLM post-training, RLHF/RLAIF, or preference optimization.
Experience with distributed training, multi-GPU workloads, or large-scale inference.
Familiarity with parallelism strategies such as data, tensor, pipeline, or sequence parallelism.
Experience with training and inference frameworks, orchestration systems, or cluster environments.
Understanding of GPU performance, memory management, networking, and distributed communication.
Experience building reliable research infrastructure, profiling systems, or debugging distributed workloads.

Nice To Haves

Familiarity with containerization, experiment tracking, and cloud or cluster computing is beneficial.
Publications at leading venues such as ICML, NeurIPS, ICLR, MLSys, CVPR, ICCV, or ECCV are preferred.

Responsibilities

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.
Build tools for experiment configuration, checkpointing, logging, monitoring, and reproducibility.
Profile end-to-end training pipelines and resolve performance, memory, and communication bottlenecks.
Support on-policy and off-policy training workflows using verifiable, preference-based, or model-generated feedback.
Collaborate with researchers to translate experimental requirements into production-quality infrastructure.
Document system designs and contribute to technical reports and publications.