Spring/Summer 2027 PhD Large Language Model Engineer 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 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: 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: Spring/Summer 2027 Co-Op: January 25, 2027 - August 13, 2027 WHAT YOU WILL BE DOING: We are seeking a highly motivated LLM Research Intern pursuing a PhD in Machine Learning (ML) Systems, High-Performance Computing (HPC), or related fields.

This internship offers an opportunity to contribute to cutting-edge research at the intersection of large language models (LLMs), distributed computing, and system optimization. The selected intern will work closely with our research and engineering teams to explore innovative techniques to enhance the efficiency, scalability, and performance of LLM training and inference on modern hardware architectures.

Conduct research on scalable training and inference of large language models, focusing on ML systems and HPC techniques Develop and optimize distributed training frameworks, model parallelism strategies, and efficient resource management for large-scale AI workloads. Explore hardware-aware optimizations, including algorithm-hardware co-optimization, sparsity-aware computation, quantization, and memory-efficient techniques for LLMs.

Implement and benchmark state-of-the-art ML system optimizations, leveraging high-performance computing techniques. Collaborate with researchers and engineers to publish findings in top-tier conferences (NeurIPS, ICML, MLSys, SC, etc.). WHO WE ARE LOOKING FOR: Currently pursuing a PhD in Computer Science, Electrical Engineering, or a related field with a focus on ML Systems, HPC, or AI Infrastructure.

Strong background in machine learning, distributed systems, and parallel computing. Experience with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and large-scale model training. Proficiency in Python and C++, with experience in performance profiling and optimization. Knowledge of GPUs, ASICs, distributed training paradigms (e.g., data/model pipeline parallelism, FSDP, ZeRO, DeepSpeed, Megatron-LM).

Familiarity with HPC techniques, including MPI, Rcom/CUDA, RCCL/NCCL, and high-speed networking technologies. Prior research experience in scalable deep learning systems, large-scale LLM training, or AI acceleration. Experience with AI compiler optimizations (e.g., Triton, XLA, MLIR) 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 . <span style="font-family: 'Arial',sans-serif; color: blac