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

Location: San Jose, CA, USA

Onsite/Hybrid: This role requires the student to work full time ( 40 hours a week), either in a hybrid or onsite work structure throughout the duration of the co-op/intern term.

Duration: Spring/Summer 2027 Co-Op: January 25, 2027 - August 13, 2027

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 .

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.