4 days ago
Santa Clara, Cuba or San Jose, CA, USAIntern
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 model optimization methods including quantization, low-precision inference, sparsity, pruning, distillation, and parameter-efficient fine-tuning.
- Contribute to AI frameworks, libraries, execution runtimes, deployment technologies, compiler systems, graph optimization, and runtime technologies.
- Develop or evaluate parallel and distributed computing methods for scalable training and inference.
- Build automated evaluation systems, containerized development environments, CI/CD pipelines, internal tools, and developer productivity solutions for AI workflows.
- Collaborate with software engineers, researchers, architects, platform teams, and project stakeholders.
Requirements
- 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, project work, or strong technical interest in AI model optimization, GPU or accelerator kernels, AI frameworks, compilers, performance analysis, parallel computing, distributed systems, MLOps, automated evaluation, containerized environments, or AI-assisted coding.
- Familiarity with development and build tools such as Git, CMake, Make, Conda, or Docker.
- Research publications, preprints, open-source contributions, or participation in the AI and machine learning developer community are preferred but not required.
Benefits
- Full-time, 40-hour-per-week hybrid or onsite work in San Jose or Santa Clara, California, for the duration of the internship or co-op.
- Spring/Summer co-op runs January 25, 2027 through August 13, 2027.
- Summer internship runs May 24, 2027 through August 13, 2027 for semester students, or June 21, 2027 through September 10, 2027 for quarter students.
- Summer/Fall co-op runs May 24, 2027 through December 10, 2027 for semester students, or June 21, 2027 through December 10, 2027 for quarter students.
- AMD benefits are described in the AMD benefits at a glance materials.
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.