PhD Intern: AI Hardware & Memory Systems Architect

Micron Technology, Inc; Folsom, Northern (CA, KY); Hybrid; USD 69,000 - 83,000; Full time

Benefits offered by this job

Health insurance

Job summary

Micron Technology, Inc invites a PhD intern to advance memory and system architecture concepts for AI accelerators in Folsom, CA. You will model performance, analyze AI workloads, and optimize memory hierarchies across HBM and interconnects while collaborating with experienced teams.

The role emphasizes research methods, scalable designs, and cross-functional collaboration in a fast-moving AI infrastructure context.

Qualifications

• PhD candidate in a technical field relevant to AI hardware and memory systems.
• 0-2 years of research, internship, or project experience in computer architecture or memory systems.
• Knowledge of memory system architecture including caches, HBM, DRAM, and interconnects.
• Experience with performance modeling, architectural simulation, or system analysis.
• Programming experience in Python or C++; strong technical communication.

Responsibilities

• Research and prototype memory and system architecture concepts for AI accelerators.
• Develop performance models to assess bandwidth, latency, power, and scalability.
• Analyze AI training, inference, and HPC workloads for bottlenecks.
• Explore memory hierarchy, memory technologies, and interconnect optimizations.
• Collaborate with cross-functional teams and leverage AI/LLM tools in research workflows.

Skills

Python

C++

Performance modeling

AI hardware concepts

Analytical skills

Education

PhD in Computer Architecture / EE / CS

Tools

SystemC

GPU profiling tools

Emulation platforms

CUDA-based development envs

PyTorch / TensorFlow

Job description

Micron Technology, Inc invites a PhD intern to advance memory and system architecture concepts for AI accelerators in Folsom, CA. You will model performance, analyze AI workloads, and optimize memory hierarchies across HBM and interconnects while collaborating with experienced teams.

The role emphasizes research methods, scalable designs, and cross-functional collaboration in a fast-moving AI infrastructure context.