2027 PhD AI Model Optimization & Software Engineer Intern/Co-op

About the role

AMD believes technology can solve the world’s most important challenges, from advancing healthcare and scientific discovery to powering AI and everyday technologies. As an AMD intern or co‑op you will work at the epicenter of the AI ecosystem alongside experts and industry pioneers, contributing to projects that impact millions of end‑users worldwide. The experience is designed to be a launchpad for your future career.

Job Details

• Location: San Jose, CA or Santa Clara, CA
• Work structure: Full‑time (40 hours/week), hybrid or onsite
• Duration:
  • Spring/Summer Co‑op: January 25 2027 – August 13 2027
  • Summer Internship (Semester students): May 24 2027 – August 13 2027
  • Summer Internship (Quarter students): June 21 2027 – September 10 2027
  • Summer/Fall Co‑op (Semester students): May 24 2027 – December 10 2027
  • Summer/Fall Co‑op (Quarter students): June 21 2027 – December 10 2027

Responsibilities

• Develop, benchmark, and optimize AI software for training, fine‑tuning, and inference across CPU, GPU, and accelerator platforms.
• Profile AI workloads, identify hardware and software bottlenecks, and implement performance improvements across compute, memory, communication, framework, and runtime layers.
• Design, prototype, and optimize GPU or CPU kernels using HIP, CUDA, OpenCL, Triton, or related accelerator programming models.
• Research and implement AI model optimization methods such as quantization, low‑precision inference, sparsity, pruning, distillation, and parameter‑efficient fine‑tuning.
• Contribute to AI frameworks, libraries, execution runtimes, and deployment technologies such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.
• Develop or evaluate parallel and distributed computing methods, including collective communication operations and scalable training or inference techniques.
• Explore compiler, graph optimization, kernel‑generation, and runtime technologies that improve the efficiency and portability of AI workloads.
• Build automated evaluation systems, containerized development environments, CI/CD pipelines, internal tools, and developer‑productivity solutions for AI workflows.
• Use AI‑assisted development tools and coding agents to support software development, testing, debugging, and optimization.
• Collaborate with software engineers, researchers, architects, platform teams, and project stakeholders to advance next‑generation AI software capabilities.

Qualifications

• Currently enrolled in a U.S.-based PhD program in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related technical field.
• Programming experience in Python and/or C/C++.
• Experience with one or more AI frameworks or runtimes (e.g., PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, SGLang).
• Experience, coursework, research, or project work in one or more of the following areas:
  • AI model inference, training, fine‑tuning, or performance optimization
  • Quantization, low‑precision computing, sparsity, pruning, or distillation
  • GPU or accelerator kernel development
  • AI frameworks, libraries, compilers, graph optimization, or execution runtimes
  • Performance profiling, benchmarking, debugging, or workload analysis
  • Parallel computing, distributed systems, or collective communications
  • MLOps, DevOps, automated evaluation, CI/CD, or containerized environments
  • AI‑assisted coding, coding agents, or agentic development workflows
• Familiarity with software development tools such as Git, CMake, Make, Conda, Docker, or related build technologies.
• Research publications, preprints, open‑source contributions, or participation in the AI and machine‑learning developer community are preferred but not required.
• Must be eligible to work in the United States; this role is not eligible for visa sponsorship.