Principal AI Systems Performance Engineer

Micron Technology • Austin, TX

3d

About The Position

Micron Technology’s Data Center Workload Engineering team studies how real AI workloads use compute, memory, interconnects, and storage. The team turns system-level measurements into insights that help shape future Micron DRAM (DDR and LPDDR), HBM, CXL, and storage products.

As a Principal AI Systems Performance Engineer, you will direct experiments on AI training, inference, and agentic workloads across complete server systems. You will identify where bandwidth, capacity, latency, power, and data movement restrict performance, and translate findings into product requirements and guidance for engineering, product, and customer teams. This work will connect current measurements with the demands of future AI infrastructure.

Requirements

• Bachelor’s degree with at least 10 years of experience; master’s degree with 5 or more years; or Ph.D. with demonstrated experience in a related field.
• Hands-on experience profiling and benchmarking multi-GPU AI systems across single-node and multi-node environments.
• Understanding of transformer workloads, memory behavior, distributed systems, and agentic AI infrastructure.
• Experience with PyTorch, an AI serving platform, Python, and at least one of C, C++, CUDA, or Triton.
• Ability to lead controlled experiments and communicate results to technical and business audiences.

Nice To Haves

• Experience with memory tiering, KV-cache management, or emerging AI workloads.
• Experience with disaggregated infrastructure or performance modeling and simulation.
• Relevant research, patents, open-source contributions, or other substantial technical work.

Responsibilities

• Develop and perform controlled, representative AI experiments on current and pre-production CPU-GPU systems, evaluating memory and storage performance, power, and thermal tradeoffs.
• Profile workloads from GPU memory through system memory, CXL-attached memory, storage, and interconnects; assess performance from node to cluster scale, including efficiency, latency, utilization, scalability, and tail behavior.
• Evaluate data placement, migration, prefetching, compression, and tiering approaches for KV cache, model state, and other AI data.
• Build and validate performance models, simulations, traces, and reusable benchmarking tools to forecast future system requirements.
• Lead cross-functional projects and translate outcomes into product requirements, recommendations, publications, demonstrations, and customer mentorship.

Benefits

• Choice of medical, dental, and vision plans.
• Programs to protect income in the event of illness or injury.
• Paid family leave.
• Robust paid time-off program.
• Paid holidays.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal