Intern - AI Hardware and Memory Systems

## At Micron Technology, we transform how the world uses information to enrich life for all. The HBM Architecture team is at the forefront of developing next-generation memory solutions that enable advances in artificial intelligence, high-performance computing, and data-intensive applications. The team researches and evaluates innovative memory and system architecture concepts, working across disciplines to optimize performance, power efficiency, and scalability for future computing platforms. Join a collaborative environment where innovative research is transformed into technologies that shape the future of AI infrastructure. As a PhD Intern on the HBM Architecture team, you will research and prototype advanced memory and system architecture concepts for next-generation AI accelerators. You will develop performance models, analyze AI workloads, and evaluate how memory hierarchy and interconnect design choices impact system performance, power efficiency, and scalability. This role offers the opportunity to collaborate with experienced architects and engineers while contributing to the future of AI computing systems. ## Responsibilities - Research and evaluate advanced memory and system architecture concepts for AI accelerators, including GPUs, TPUs, and custom accelerator designs. - Develop and analyze performance models to assess bandwidth, latency, power, scalability, and overall system efficiency. - Analyze AI training, inference, and HPC workloads to identify memory and system performance bottlenecks. - Investigate memory hierarchy, HBM, DRAM, cache, storage, and interconnect optimizations to improve system performance. - Collaborate with cross-functional engineering teams and explore the use of AI/LLM-based tools to enhance architecture research and development workflows. ## Minimum Qualifications - Currently pursuing a PhD in Computer Architecture, Electrical Engineering, Computer Engineering, Computer Science, or a related technical field. - 0-2 years of relevant experience through research, internships, academic projects, or industry experience in computer architecture, memory systems, or AI hardware. - Knowledge of memory system architecture, including caches, HBM, DRAM, memory hierarchy, and interconnect concepts. - Experience with performance modeling, architectural simulation, system analysis, or related research methodologies. - Programming experience in Python, C++, or similar programming languages. - Strong analytical, problem-solving, and technical communication skills. ## Preferred Qualifications - Research experience in AI accelerators, GPU architecture, memory systems, interconnects, or high-performance computing. - Experience with AI/ML frameworks such as PyTorch, TensorFlow, or CUDA-based development environments. - Familiarity with architectural simulators, SystemC, GPU profiling tools, emulation platforms, or performance analysis tools. - Knowledge of rack-scale systems, scale-up/scale-out architectures, and advanced interconnect technologies. - Publications, conference papers, or demonstrated research contributions in computer architecture, AI hardware, memory systems, or related fields.