AI Training Systems & Performance Intern (PhD)

AMD; San Jose (CA); On-site; USD 34,000 - 55,000; Full time

Job summary

AMD is seeking a highly motivated AI Training Systems & Performance Engineering PhD Intern/Co-Op to join our AI ecosystem in San Jose. You'll help accelerate the adoption and optimization of AI training workloads on AMD Instinct GPUs while contributing to next-gen performance solutions.

You'll work with software engineers, architects, and AI specialists to bring up training workloads, profile bottlenecks, and develop tooling that enhances scalability, efficiency, and developer

Qualifications

• Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related technical discipline.
• Strong programming experience in Python and/or C++.
• Hands-on experience implementing, training, and debugging deep learning models using frameworks such as PyTorch, JAX, TensorFlow, vLLM, or SGLang.

Responsibilities

• Develop and optimize large-scale AI training and fine-tuning workloads on AMD Instinct GPUs.
• Bring up foundation models and training frameworks, adapting implementations for AMD hardware and establishing baselines.
• Profile workloads to identify bottlenecks across GPUs, CPUs, memory, networking, and communication.

Skills

Python programming

C++ programming

Deep learning

Distributed training

Performance profiling

Education

PhD candidate in Computer Science / Computer Engineering / Electrical Engineering / AI

Tools

PyTorch

TensorFlow

JAX

vLLM

SGLang

MPI

NCCL/RCCL

Job description

AMD is seeking a highly motivated AI Training Systems & Performance Engineering PhD Intern/Co-Op to join our AI ecosystem in San Jose. You'll help accelerate the adoption and optimization of AI training workloads on AMD Instinct GPUs while contributing to next-gen performance solutions.

You'll work with software engineers, architects, and AI specialists to bring up training workloads, profile bottlenecks, and develop tooling that enhances scalability, efficiency, and developer