Meta's Manufacturing Quality Engineering team is building the infrastructure that powers AI at a global scale. As a Product Quality Engineer on the Hardware Engineering team, you will play a critical role in bringing next-generation AI/ML hardware from concept to mass production directly enabling the compute capacity that drives Meta's products. This role sits at the intersection of engineering innovation and operations, where you will execute on the manufacturing readiness plan for advanced compute and GPU platforms, partner with design engineers and global manufacturing partners, and help ensure Meta's hardware fleet meets the highest standards of quality and reliability at hyperscale volumes.

Qualifications:

Bachelor's degree in Electrical Engineering, Mechanical Engineering, Manufacturing Engineering, or a relevant technical field, or equivalent practical experience 6+ years of experience in manufacturing engineering or a related field Experience in program and project management, supporting complex hardware products from NPI through to high-volume mass production (MPP) launch Working knowledge of hardware architecture with hands-on experience in the testing and validation of compute and GPU systems Background in process development, bring-up, and qualification, encompassing final assembly, test, and pack-out processes Experience partnering with stakeholder teams to drive alignment on technical decisions, through proposals, design reviews, or presentations Advanced degrees in Electrical Engineering, Mechanical Engineering, Manufacturing Engineering, or a related field Experience identifying and scoping ambiguous technical problems, defining actionable next steps, and driving resolution Experience working with international contract manufacturers Experience with data center hardware manufacturing at hyperscale Scripting of test cases for compute / storage / AI/ML servers and racks Experience with test automation frameworks and manufacturing execution systems (MES) Experience working within quality management systems such as ISO 9001 or in a hardware manufacturing environment Background in reliability engineering methodologies, including accelerated life testing and failure mode analysis Experience with GPU architectures, high-performance computing systems, or AI/ML infrastructure hardware Experience of proactively working on issues may not be clearly defined