Responsibilities
- 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.
JOB DETAILS:
Location:
Austin, Texas, USA
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.
WHO WE ARE 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 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 Git, CMake, Make, Conda, Docker, or related development and build technologies.
Research publications, preprints, open-source contributions, or participation in the AI and machine learning developer community are preferred but not required.
If you are excited about advancing AI software and bring experience in at least one of the areas above, we encourage you to apply.
Note:
This role is not eligible for visa sponsorship.
Qualifications
