Hardware System Validation Engineer

Summary

Join the system validation team at Apple's Hardware Engineering, where we validate system-level performance of Apple SoCs and other key modules (cameras, displays, and other state-of-the-art sensors) in a system environment that mirrors real product use.

Minimum Qualifications

Bachelor's degree or higher in Engineering or a related field 3+ years of experience with hardware/software architecture and interoperability of complex systems Scripting proficiency in Python; comfortable building and maintaining automation frameworks Experience in one of the following: hardware system design and integration, SoC design/integration, debug/testing, or validation

Preferred Qualifications

Experience with SoC bring up, familiar with computer architectures (cache, memory hierarchy, data pipelines) and logic design Experience with bringing-up and testing of camera, display or any other modules in consumer electronics Experience in silicon validation software engineering or related field Experience with embedded system debug Experience with lab equipment such as oscilloscopes and thermal chambers Strong proficiency in Python and Lua; proficient in Shell scripting. Familiarity with industry standards (USB, PCIe, DDR, etc.) General software development experience (Python, C++, log review, code review) Strong communication and collaboration skills

Description

In this role, you will: - Primarily validate Apple SoCs, while also running testing on other modules such as cameras, displays, and sensors that ship in Apple products - Work closely with module designers and functional test writers to understand high-level architectures of each IP block within Apple SoCs or other modules, and define applicable validation plans - Develop and execute these system tests, leveraging scripts and automation frameworks, on a large volume of engineering prototypes throughout the product life cycles - Serve as the first line of debug when issues occur — triaging, isolating, and bucketizing failures - Partner with domain experts to define DoEs (Design of Experiments), develop root-cause theories, and test possible workarounds - Development of automated testing for power consumption data collection from our battery and power supply systems - Creation of new test methodologies to expand test coverage to new modalities including optical / computer-vision based testing - Optimize test and calibration flows for hardware sub-systems. Apply AI/ML techniques for test data mining, anomaly detection, and factory data analytics - Travel internationally ~10% of the time