Spring/Summer 2027 PhD Large Language Model Engineer Co-op

Advanced Micro Devices, Inc•San Jose, CA

3d•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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)

Responsibilities

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.).