AI Infrastructure & HBM Architecture Engineer New Grad

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. We are seeking a highly motivated AI Infrastructure & HBM Architecture Engineer to support the exploration of next‑generation AI memory and system environments. You will help analyze emerging infrastructure trends, contribute to performance modeling efforts, and support tool development that informs HBM architecture decisions. You will work closely with senior engineers across DTPCO, Design, and Product/System teams, gaining hands‑on exposure to how AI workloads influence memory system design and architectural intercept strategies. ## Key Responsibilities You will contribute to projects such as: - Researching emerging trends in compute, networking, and storage infrastructure to understand how future AI workloads will evolve. - Supporting HBM architecture analysis by collecting and synthesizing data that informs bandwidth, latency, and system‑level memory requirements. - Helping develop or refine simulation frameworks used to model AI cluster performance and memory subsystem behavior. - Assisting in building first‑order performance models for HBM bandwidth, latency, GPU/CPU interactions, and memory hierarchy trade‑offs. - Participating in building trend dashboards using CSP benchmarks and industry datasets. - Collaborating with DTPCO mentors to explore architectural intercept opportunities relevant to next‑generation AI systems. ## Required Qualifications - Currently pursuing a Master’s degree or PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field. - Strong foundational understanding of computer architecture, digital systems, and hardware/software interaction. - Interest in memory subsystem design (HBM, DDR, caches) and how it impacts AI/ML workloads. - Deep understanding of LLM pipeline and critical bottlenecks. - Programming experience in Python, C/C++, or similar languages. ## Preferred Skills - Exposure to AI workload behavior, ML model training pipelines, or large-scale system bottlenecks. - Ability to turn research findings into concise technical summaries or recommendations. - Comfort learning new tools, simulation frameworks, and data‑analysis workflows.