PhD AI Systems & GPU Performance Engineering Intern

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMDis shapingthefuture.

Whether you’redesigning next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger— technology that moves the world forward.

Join us and, together, we’ll advance your career.

• Location:
  
  San Jose, CA or Santa Clara,CA
• Onsite/Hybrid:
  This role requires the student to work full time (40 hours a week), in either a hybrid or onsite work structure throughout the duration of the co-op/intern term
• Duration:
  • Spring/Summer Co-op:
    January 25, 2027 - August 13, 2027
  • Summer Internship:
    • Semester Students:
      May 24, 2027 - August 13, 2027
    • Quarter Students:
      June 21, 2027 - September 10, 2027
  • Summer/Fall Co-op:
    • Semester Students:
      May 24, 2027 - December 10, 2027
    • Quarter Students:
      June 21, 2027 - December 10, 2027

We are seeking a highly motivated PhD AI Systems & GPU Performance Engineering Intern to join our team. In this role, you will help optimize state-of-the-art AI models, training workflows, and inference applications on AMD Instinct™ GPUs using AMD's latest hardware and software technologies.

• We will involve you in profiling, benchmarking, and optimizing AI training and inference workloads using ROCm™, PyTorch, JAX, vLLM, Triton, and related performance engineering tools, helping identify bottlenecks across compute, memory bandwidth, communication, and kernel execution.
• Your responsibility will include developing reproducible benchmarking frameworks and automation scripts that enable performance validation across AI models, software stacks, runtimes, drivers, and accelerator platforms.
• We will train you to analyze and optimize end-to-end AI workflows, including large language models (LLMs) and generative AI applications, exploring techniques such as model optimization, operator fusion, scheduling strategies, mixed precision, and quantization.
• You will work closely with engineers and researchers to evaluate GPU performance, compare workload characteristics across hardware and software environments, and contribute data-driven recommendations that improve efficiency, scalability, throughput, latency, and overall system utilization.
• You get to explore cutting-edge AI systems research on AMD's newest hardware platforms while contributing profiling analysis, performance investigations, and optimization solutions that advance next-generation AI training and inference technologies.

• Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical field, with an expected graduation date after the internship concludes.
• Hands-on programming experience in Python and C/C++, with exposure to accelerator programming technologies such as CUDA, HIP, Triton, or similar frameworks.
• Experience with deep learning frameworks such as PyTorch, JAX, or Tensor Flow and familiarity with large-scale AI model training and inference concepts.
• Experience working in Linux environments, including scripting, debugging, profiling, and performance analysis.
• Exposure to GPU architecture concepts such as memory hierarchy, parallel execution, kernel optimization, communication efficiency, operator fusion, scheduling, or accelerator performance characteristics through research, coursework, or projects.
• Familiarity with profiling, benchmarking, and performance analysis methodologies using tools such as roc Profiler, ROCm Systems Profiler, Omniperf, Nsight, or equivalent…