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

About the role

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 AMD is shaping the future. Whether you’re designing 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. As an AMD intern and co-op, you’ll be placed at the epicenter of the AI ecosystem, working alongside experts and industry pioneers. You’ll do important work, learn new skills, expand your network, and gain real-world experience on projects that impact millions of end-users worldwide. Whether you’re an undergrad or a PhD student, your contributions matter—and your experience here will be a launchpad for what comes next.

Location: Austin, Texas, USA

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 technologies such as 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, such as PyTorch, TensorFlow, JAX, ONNX Runtime, vLLM, or 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