Austin, United States Role overview
About this role
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.
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
- 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:
- 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.
Note: By submitting your application, you are indicating your interest in AMD intern positions. We are recruiting for multiple positions, and if your experience aligns with any of our intern opportunities, a recruiter will contact you.
This role is not eligible for visa sponsorship.
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
Qualifications
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
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
- 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:
- 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.
Note: By submitting your application, you are indicating your interest in AMD intern positions. We are recruiting for multiple positions, and if your experience aligns with any of our intern opportunities, a recruiter will contact you.
Shanghai, China