Systems Performance Engineering Intern

Micron Technology is a world leader in memory and storage solutions, accelerating the transformation of information into intelligence. The Data Center Workload Engineering team evaluates how memory and storage products perform in complete data center environments, deploying customer-representative workloads across AI, databases, virtualization, and software-defined storage in production-class labs.

As a Systems Performance Engineering Intern, you will partner with experienced engineers on a defined performance analysis project. You will run real-world workloads on enterprise systems, collect and analyze performance data, develop automation tools, and present findings to collaborators. The role offers direct mentorship, exposure to advanced data center technologies, and hands-on experience with production-scale infrastructure.

Responsibilities

• Execute performance validation and benchmarking on enterprise servers, GPU-accelerated systems, and data center storage platforms using established methodologies.
• Configure and run customer-representative workloads, including AI inference, databases, virtualization, checkpointing, or object storage.
• Collect, validate, and analyze system and device telemetry to ensure data quality and actionable insights.
• Develop or enhance Python-based automation tools for test setup, data collection, analysis, and reporting.
• Investigate and present findings on a defined performance project, contributing code and documentation.

Minimum Qualifications

• Currently pursuing a Bachelor's, Master's, or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or related field, with graduation after the internship.
• Completed coursework in operating systems, computer architecture, systems programming, or related subjects.
• Experience with Linux environments, including command-line tools, file systems, and basic administration.
• Programming experience in Python, C, C++, or a comparable language.
• Strong analytical and communication skills, including experience with AI-assisted coding tools.

Preferred Qualifications

• Academic or internship experience in performance measurement, benchmarking, profiling, or systems analysis.
• Familiarity with tools such as perf, fio, iostat, bpftrace, or language-level profilers.
• Knowledge of storage technologies: block devices, NVMe, PCIe, or file system internals.
• Experience with Python data analysis and visualization (pandas, matplotlib).
• Exposure to AI workloads, GPU computing, databases, virtualization, distributed storage, and Git.

What You Will Gain

• Ownership of a well-defined technical project with measurable outcomes.
• Dedicated mentorship and regular technical reviews.
• Hands-on experience with production-class lab hardware, including GPU platforms and current SSDs.
• Opportunity to present results and contribute to potentially publishable work.