Intern – Memory Systems Architecture & AI Accelerators
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About this role
Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
The DRAM Systems & Architecture Lab is looking for a research-focused intern to work on next-generation memory systems for AI accelerators. The work emphasizes tiered memory architectures to enable scalable and efficient AI inference systems. The intern will operate at the intersection of hardware architecture, systems software, and workload-driven performance analysis. The intern should have practical experience evaluating computer architectures through modeling, simulation, and hardware measurement.
Modern AI workloads, particularly large language model inference, are increasingly constrained by memory capacity, data movement, and system-level inefficiencies rather than raw compute. This internship investigates how emerging memory technologies and architectural mechanisms can be composed into effective multi-tier memory systems, and how software and runtime systems must evolve to fully exploit them.
Responsibilities:
- Develop rigorous experimental methodologies for end-to-end workload evaluation
- Build research prototypes in software and/or FPGA-based hardware
- Perform quantitative analysis to explain observed performance behavior
- Contribute to internal research artifacts and potential publications
Minimum Qualifications:
- Currently enrolled in a PhD program in Computer Engineering, Electrical Engineering, or Computer Science. Intern cannot graduate prior to September 2026.
- Strong background in computer architecture and memory systems
- Experience with systems programming (C/C++ and/or low-level Python)
- Demonstrated interest in research and experimental systems work
- Comfortable reading and understanding research papers and low-level code
Preferred Qualifications:
- Prior research in memory systems, GPUs, or accelerators
- Experience with FPGA development (RTL or HLS)
- Familiarity with performance modeling, profiling, or simulation
- Coursework or publications in architecture or systems