GPU Power and Performance Characterization Engineer

In this role, you will:

• Identify and drive improvements in silicon-based characterization methodology

• Design, execute, and communicate plans to perform characterization work

• Configure system HW and SW for silicon characterization

• Lead failure triage and debug of underlying issues

• Develop, maintain, and improve automation tools

Academic coursework in computer architecture and CMOS VLSI

Experience with Power / Performance Estimation and Optimization

Experience with lab equipment

Data analysis and visualization

Experience with power measurements a strong plus

Familiarity with power-management techniques such as dynamic clock & power gating and dynamic voltage frequency scaling

Minimum Qualifications

Minimum BS

Experience with Python

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish! Dynamic, resourceful people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

The GPU Silicon Characterization Team is responsible for characterizing the behavior of Apple’s embedded GPUs and determine the best settings to optimize power and performance. We work hand-in-hand with the design, verification, productization, hardware, and software teams to enable world-class GPUs in the embedded space. In this role, you will be responsible for the planning, implementation, and execution of validation plans that focus on power and performance features within the embedded GPU. Team members are involved in test planning, test execution, status communication, data analysis, failure triage, and tool development. You will devise and run experiments to maximize performance, improve operating margins, minimize power usage and help achieve performance/Watt targets for current and future Apple embedded GPUs.