HPC & AI Co-op Intern — GPU Performance & Optimization

Advanced Micro Devices, Inc.; Austin (TX); Hybrid; USD 34,000 - 55,000; Full time

Benefits offered by this job

AMD benefits at a glance

Job summary

Advanced Micro Devices, Inc. seeks a PhD intern to join the HPC/AI research team in Austin. You will work on optimizing AI workloads for large-scale computing environments, develop novel algorithms, and explore adapting AMD tools for next-generation GPUs.

This role emphasizes collaboration with experts and hands-on benchmarking across GPU families. The ideal candidate is enrolled in a PhD program, has strong parallel computing and AI/ML fundamentals, and can code in Python and C/C++ with GPU/CPU

Qualifications

• Currently enrolled in a PhD program in Computer Science, Computational Science, Electrical/Computer Engineering, Applied Mathematics, or related field.
• Strong background in parallel computing, distributed systems, or AI/ML frameworks.
• Proficiency in programming languages such as Python, C/C++, Fortran or ROCm/CUDA.
• Experience with at least one deep learning framework (e.g., PyTorch, TensorFlow, JAX).
• Familiarity with MPI, OpenMP, or GPU programming and numerical methods.

Responsibilities

• Build, run, and analyze performance of benchmarks on GPU-accelerated platforms.
• Assess development toolchains and runtimes for performance and usability.
• Explore optimization techniques and how GPU offloading benefits workloads.
• Participate in projects at the HPC/AI intersection and contribute to experimental studies.

Skills

Python

C/C++

GPU programming

Parallel computing

AI/ML basics

Education

PhD program in CS/CompSci/EE/Applied Math

Tools

MPI

OpenMP

ROCm/CUDA

Job description

Advanced Micro Devices, Inc. seeks a PhD intern to join the HPC/AI research team in Austin. You will work on optimizing AI workloads for large-scale computing environments, develop novel algorithms, and explore adapting AMD tools for next-generation GPUs.

This role emphasizes collaboration with experts and hands-on benchmarking across GPU families. The ideal candidate is enrolled in a PhD program, has strong parallel computing and AI/ML fundamentals, and can code in Python and C/C++ with GPU/CPU