PhD AI Systems & GPU Performance Engineering Intern

Amd

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
San Jose, CASanta Clara, CA
Salary
$91,520–$137,280 / yr
Employment
Intern
Posted
2 days ago
Freshness
Confirmed live today
Closes
Sep 11, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $197k
This role $114k
$74k most similar roles pay here $259k

This role pays less than 82% of similar roles. Most pay $158,562–$235,750 — the shaded band above. At the midpoint, this role pays about $114k versus about $197k 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 379 open roles on FindRole.

Listed pay typically runs $166,400–$249,600 across 379 roles with salary data.

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At a glance

TL;DR · PhD AI Systems & GPU Performance Engineering Intern

As a 2027 PhD AI Systems & GPU Performance Engineering Intern, you will join the team to optimize state-of-the-art AI models, training workflows, and inference applications on AMD Instinct GPUs. You will perform profiling, benchmarking, and optimization of workloads using ROCm, PyTorch, JAX, vLLM, and Triton while identifying bottlenecks in compute, memory bandwidth, communication, and kernel execution. Your daily responsibilities include developing reproducible benchmarking frameworks, automation scripts, and analyzing end-to-end workflows for large language models and generative AI applications through techniques like operator fusion, mixed precision, and quantization. You will utilize Python, C/C++, and Linux environments while leveraging tools such as rocProfiler, Omniperf, and Nsight to evaluate GPU performance. This role focuses on the technical challenge of improving efficiency, scalability, throughput, and latency for next-generation AI training and inference technologies across various hardware and software platforms.

What you'll do

  • Profile, benchmark, and optimize AI training and inference workloads using ROCm, PyTorch, JAX, vLLM, and Triton.
  • Identify performance bottlenecks across compute, memory bandwidth, communication, and kernel execution.
  • Develop reproducible benchmarking frameworks and automation scripts for performance validation across software stacks and hardware platforms.
  • Analyze and optimize end-to-end AI workflows including large language models and generative AI applications.
  • Implement optimization techniques such as model quantization, mixed precision, operator fusion, and scheduling strategies.
  • Evaluate GPU performance to provide data-driven recommendations for improving efficiency, scalability, throughput, and latency.
  • Conduct profiling analysis and performance investigations on AMD's latest hardware platforms.

What we're looking for

  • Must be currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical field.
  • Expected graduation date must be after the internship concludes.
  • Must have hands-on programming experience in Python and C/C++.
  • Must have exposure to accelerator programming technologies such as CUDA, HIP, Triton, or similar frameworks.
  • Experience with deep learning frameworks like PyTorch, JAX, or TensorFlow is required.
  • Experience working in Linux environments including scripting, debugging, profiling, and performance analysis is required.
  • Exposure to GPU architecture concepts such as memory hierarchy, parallel execution, and kernel optimization is required.
  • Familiarity with profiling and benchmarking tools like rocProfiler, ROCm Systems Profiler, Omniperf, or Nsight is required.

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