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

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 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: 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     WHAT YOU WILL BE DOING:   We are seeking highly motivated AI Model Optimization & Software Engineer Interns/Co-op to join our teams. We are recruiting for multiple opportunities across AI model optimization, framework engineering, performance analysis, GPU computing, distributed systems, and AI software infrastructure.   Depending on your background, interests, and the needs of the team, you may: 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.

LOOKING FOR: We recognize that candidates may bring strengths in different areas of AI Model Optimization & AI Software. You are not expected to have experience in every technology or technical area listed below. If you have experience, academic research, project work, or a strong technical interest in one or more of these areas, we encourage you to apply. Currently enrolled in a U.S.-based PhD program in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Electrical