Hardware Systems Architecture Intern, Performance & Workload Analysis

This is a 12 week internship.

Responsibilities

Translate target use cases into hardware requirements - budgeting compute, memory bandwidth, storage throughput, power, and thermal for experiences such as mixed-reality passthrough, always-on AI assistant, and camera capture Contribute to architecture trade studies comparing SoC, memory, storage, and ISP options against performance, power, thermal, cost, and form-factor limits, using analytical models (e.g. roofline, bandwidth and power budgets) validated against measurements Measure platform behavior under load at the system level using power-rail instrumentation, thermal chambers, and hardware performance counters, and use the data to calibrate models Identify where performance is capped by hardware - memory bandwidth, power delivery, thermal headroom, I/O - rather than software Characterize how requirements shift across product classes, from a tethered or high-power headset to a battery-limited wearable Present findings to silicon, hardware, and product leads, and document results so they can feed into supplier evaluations and next-generation platform definitions

Qualifications

Currently pursuing a Master's degree or higher in Electrical Engineering, Computer Engineering, or a related field Coursework or research experience in computer architecture, SoC design, or embedded systems, including memory hierarchy, interconnects, and power management Experience with quantitative modeling and data analysis in C++, Python, or MATLAB Intent to return to full-time degree program after completion of the internship Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Currently pursuing a PhD in Electrical Engineering, Computer Engineering, or a related field Research or project experience in performance and power modeling, workload characterization, or architectural simulation Hands-on experience with system-level measurement: power-rail instrumentation, thermal characterization, or hardware performance counters and profiling tools Familiarity with mobile or embedded SoC platforms, or with AR/VR, camera/ISP, or on-device ML workloads Experience communicating quantitative analysis to cross-functional engineering partners