Technical Lead Engineer — Power Validation & Characterization

About the job

Meta's Infrastructure team delivers scalable and reliable custom ASIC solutions to meet Meta's data center needs. Ramping to production and solving data center scaling and deployment challenges requires a systems-based approach to silicon bring-up and validation. The team works on productization: early architecture and design inputs, pre-silicon validation on emulation platforms, first-silicon bring-up, and post-silicon characterization across process, voltage, and temperature corners. We write tests, build bare-metal environments and reusable frameworks that scale from pre-silicon platforms to large fleets of ASICs in the lab, and we build and maintain extensive lab automation to do it at volume and velocity.

Responsibilities

Own the end-to-end validation and characterization strategy for Meta custom silicon programs in your domain, from pre-silicon emulation through post-silicon bring-up and PVT characterization Define the test plan: measurement and benchmarking methodology, instrumentation, functional and performance coverage, corner coverage across process/voltage/temperature, and pass/fail criteria Establish and document best practices for measurement rigor, accuracy, and correlation across bench, emulation, and ATE/production data Drive validation of domain feature sets (power or networking) on emulation platforms pre-silicon and on real silicon post-silicon Review schematics, power delivery networks, high-speed interface designs, and board/platform configurations with hardware and design teams; provide early design and debuggability inputs Write and review automation and analysis code that scales characterization from a single bench to many units in the lab Lead debug of complex cross-stack issues — silicon, firmware, driver, board, thermal, and fabric/switch layers — and drive root cause to closure with design, software, hardware, ODM, and vendor partners Provide technical guidance and planning for a team of validation engineers; mentor engineers and improve standards for data quality and rigor Partner with manufacturing, production, and data center teams to transfer characterization learnings into production screens and fleet health monitoring