About this role
Summary: The AI Machine Learning Engineer Intern will optimize end-to-end training and inference pipelines on AMD Instinct GPUs using ROCm software stacks. Responsibilities include exploring new models, developing numerical techniques for efficiency, and building reproducible benchmarks to improve AI performance.
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, 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: San Jose, CA, USA
Onsite: This role requires the student to work full time (40 hours a week), onsite work structure throughout the duration of the co-op/intern term.
Duration: Summer 2027 Internship:Semester Schools: May 24, 2027 – August 13, 2027
Quarter Schools: June 21, 2027 – September 10, 2027
WHAT YOU WILL BE DOING:
We are seeking a highly motivated AI Model and Application Engineering co-op to join our team. Specific projects depend on the candidates and the company's interests and needs. Some examples are as follows.
You will optimize end-to-end training and inference pipelines on AMD Instinct GPUs using the ROCm software stack (PyTorch/JAX + HIP/Triton, RCCL, rocBLAS/hipBLAS), with a focus on throughput, latency, and cost.
You will explore new models and kernel-level design tradeoffs (e.g., fast, accurate, and efficient models, attention variants, operator fusion, scheduling) and quantify their impact using rigorous benchmarking.
You will develop and evaluate numerical techniques (e.g., quantization-aware and mixed-precision training) that improve accuracy-performance balance at scale.
Your responsibilities will include building reproducible benchmarks and experiment harnesses, profiling applications, identifying bottlenecks, and landing measurable improvements.
You will help produce clear experimental reports with code artifacts that can be upstreamed to internal repos or open-source where applicable (e.g., ROCm, Triton kernels, PyTorch extensions).
WHO WE ARE LOOKING FOR:
You are currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical field.
You have demonstrated research or advanced project experience in AI systems, machine learning infrastructure, high-performance computing, computer architecture, or large-scale deep learning.
You have deep hands-on experience training, optimizing, or serving large-scale AI models using frameworks such as PyTorch or JAX, including distributed training technologies such as DeepSpeed, FSDP, Megatron-LM, or similar frameworks.
You have strong experience with GPU performance engineering, kernel optimization, and accelerator programming using CUDA, HIP, Triton, or related technologies, along with a solid understanding of GPU architecture concepts, including memory hierarchies, tensor cores/MFMA, operator fusion, and scheduling techniques.
You have experience applying profiling, benchmarking, and performance modeling methodologies, including roofline analysis, to evaluate system utilization, identify bottlenecks, and deliver measurable improvements in AI training and inference performance.
You have expertise in AI model efficiency techniques such as quantization (PTQ/QAT), low-precision formats (FP8, FP4, INT8, or Ternary), mixed-precision training, speculative decoding, and energy-efficient AI training and inference, with a proven ability to build rigorous and reproducible experiments that drive impactful optimization outcomes.
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.