AI Systems & Infrastructure Intern for LLMs & GPUs

Micron; Austin (TX); On-site; USD 34,440,000 - 48,216,000; Full time

Benefits offered by this job

Medical coverage

Paid time off

Paid holidays

Mentorship opportunities

Job summary

Micron Technology is seeking an AI Systems Software Engineering Intern to work with senior engineers on advanced systems software for LLMs and Agentic AI applications. You will help characterize performance and scalability of AI inference and training on GPU platforms and heterogeneous memory systems.

The role emphasizes profiling, experimentation frameworks, and memory-management techniques, with collaboration across engineering and research teams to influence future AI infrastructure and

Qualifications

• Currently pursuing a Master's or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Demonstrated experience with AI systems, machine learning systems, computer systems research, or systems software development through coursework, research, or projects.
• Understanding of Large Language Models (LLMs), including transformer execution, attention mechanisms, KV cache behavior, batching, token-level latency, throughput, and memory performance considerations.

Responsibilities

• Develop and enhance systems software, profiling tools, and experimentation frameworks for LLM training, LLM inference, and Agentic AI workloads.
• Design, implement, and evaluate memory- and state-management techniques for AI serving environments.
• Characterize and optimize AI workload execution across GPUs, CPUs, memory subsystems, storage, and distributed infrastructure.

Skills

Python

C/C++

Linux

AI systems

GPU profiling

PyTorch

Education

Master's or PhD in CS/CE/EE

Tools

PyTorch

TensorRT-LLM

NVIDIA GPUs

vLLM

Job description

Micron Technology is seeking an AI Systems Software Engineering Intern to work with senior engineers on advanced systems software for LLMs and Agentic AI applications. You will help characterize performance and scalability of AI inference and training on GPU platforms and heterogeneous memory systems.

The role emphasizes profiling, experimentation frameworks, and memory-management techniques, with collaboration across engineering and research teams to influence future AI infrastructure and