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Hardware Systems Engineer, NPI AI

Job in Menlo Park, San Mateo County, California, 94029, USA
Listing for: Meta
Full Time position
Listed on 2026-10-18
Job specializations:
  • Engineering
    Systems Engineer, Hardware Engineer, Test Engineer
Salary/Wage Range or Industry Benchmark: 140000 - 210000 USD Yearly USD 140000.00 210000.00 YEAR
Job Description & How to Apply Below
# Hardware Systems Engineer, NPI AIMenlo Park, CA Engineering+ 4 more Apply nowMeta is seeking a Hardware Systems Engineer to support the new product introduction (NPI) of next-generation AI and high-performance computing infrastructure for large-scale data center deployments. In this role, you will work at the intersection of AI silicon, server systems, and data center operations, partnering with hardware design, firmware, software, networking, and capacity engineering teams to validate and scale cutting-edge AI hardware systems from early bring-up through production readiness.

---## Hardware Systems Engineer, NPI AI Responsibilities
* Drive system validation strategies for AI and HPC hardware platforms, including AI accelerators, GPU clusters, and high-bandwidth memory subsystems in data center environments.
* Perform hands-on bring-up, characterization, and validation of AI server systems and associated components such as PCIe, NVLink, DRAM, and high-speed networking fabrics.
* Develop and maintain test specifications, validation procedures, and debug guides tailored to AI infrastructure NPI programs.
* Investigate and root-cause complex system failures spanning silicon, firmware, software, and hardware layers in collaboration with cross-functional engineering teams.
* Triage and track hardware and firmware defects through resolution while maintaining forward progress on NPI program milestones.
* Identify gaps in test coverage and contribute improvements to test methodologies, tooling, and automation frameworks across the NPI lifecycle.
* Partner with AI platform and capacity engineering teams to define acceptance criteria and deployment readiness standards for new AI hardware systems.
* Support data collection, analysis, and reporting efforts to surface systemic hardware quality trends.
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