AI Model Optimization Engineer Intern

AMD; Austin (TX); Hybrid; USD 28,000 - 48,000; Full time

Benefits offered by this job

AMD benefits at a glance

Job summary

AMD is seeking highly motivated AI Model Optimization & Software Engineer Interns/Co‑op in Austin, Texas. You will join multiple teams across AI model optimization, framework engineering, and performance analysis, working on CPU/GPU/inference, and contributing to AI frameworks and runtimes.

Ideal candidates are PhD students in computer science or related fields, with Python/C++ experience and familiarity with PyTorch, TensorFlow, JAX, ONNX Runtime, or similar tools.

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 AI frameworks or runtimes such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or SGLang.
• Experience or coursework in AI model inference, training, or performance optimization.
• Familiarity with software development tools like Git, CMake, Make, Conda, Docker.

Responsibilities

• Develop, benchmark, and optimize AI software for training, fine-tuning, and inference across CPU, GPU, and accelerator platforms.
• Profile AI workloads and identify bottlenecks to implement performance improvements.
• Design and optimize GPU or CPU kernels using HIP, CUDA, OpenCL, or Triton.
• Contribute to AI frameworks, libraries, and deployment technologies such as PyTorch, TensorFlow, ONNX Runtime.

Skills

Python

C/C++

Education

PhD candidate in CS/CE/AI/ML/EE

Tools

PyTorch

TensorFlow

JAX

ONNX Runtime

vLLM

SGLang

Job description

AMD is seeking highly motivated AI Model Optimization & Software Engineer Interns/Co‑op in Austin, Texas. You will join multiple teams across AI model optimization, framework engineering, and performance analysis, working on CPU/GPU/inference, and contributing to AI frameworks and runtimes.

Ideal candidates are PhD students in computer science or related fields, with Python/C++ experience and familiarity with PyTorch, TensorFlow, JAX, ONNX Runtime, or similar tools.