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

AMD; San Jose (CA); Hybrid; USD 50,000 - 70,000; Full time

Job summary

AMD invites PhD students to join as an LLM Research Intern in San Jose, CA for Spring/Summer 2027. You’ll work with research and engineering teams on scalable training and inference of large language models, exploring ML systems, HPC, and system optimizations on modern hardware.

This full-time co-op offers hands-on experience, collaboration, and opportunities to publish findings at top conferences, with a focus on performance, scalability, and energy-efficient AI workloads.

Qualifications

• Pursuing a PhD in CS/EE with focus on ML Systems, HPC, or AI Infrastructure.
• Strong background in ML, distributed systems, and parallel computing.
• Experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and large-scale model training.
• Proficiency in Python and C++, with experience in performance profiling and optimization.
• Knowledge of GPUs, distributed training paradigms (data/model parallelism, FSDP, ZeRO, DeepSpeed, Megatron-LM).
• Familiarity with HPC techniques (MPI, RCCL/NCCL, CUDA) and high-speed networking.
• Prior research in scalable DL systems, large-scale LLM training, or AI acceleration.
• Experience with AI compiler optimizations (Triton, XLA, MLIR).

Responsibilities

• Conduct research on scalable training and inference of large language models.
• Develop and optimize distributed training frameworks and model parallelism strategies.
• Explore hardware-aware optimizations and memory-efficient techniques for LLMs.
• Implement and benchmark state-of-the-art ML system optimizations.
• Collaborate with researchers and engineers to publish findings at top conferences.

Skills

Distributed systems

Parallel computing

Machine learning

Performance optimization

Python

C++

PyTorch

TensorFlow

JAX

GPUs

AI infrastructure

Education

PhD in Computer Science or Electrical Engineering

Focus on ML Systems / HPC / AI Infrastructure

Tools

MPI

CUDA

RCCL/NCCL

DeepSpeed

Megatron-LM

Triton

XLA

MLIR

Job description

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believetechnology 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 AMDis shapingthefuture.

Whetheryou’redesigning next-gen processors, enabling AI breakthroughs, orbringing leading edge products to market, every role at AMD contributes to something bigger— technologythat 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/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

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)

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.