Manufacturing Operation Manager - Reality Labs
Description
As a part of the Manufacturing Operations team at Meta, you will be responsible for managing day-to-day operations and sustaining technical excellence with our global manufacturing partners. The Manufacturing Ops team aids in developing contract manufacturer (CM) operational capabilities and implementing strategy in line with the product roadmap. The team collaborates cross-functionally to assess risks on-site, provide solutions, ensure factory readiness, meet overall ramp goals, and ensure the success of the ramp with short- and long-term capacity planning and execution to the end of life (EOL). Beyond traditional factory management, you will act as a Technical PM to drive line layout & process optimization, labor/cycle time reduction, yield maturity, and smart factory evolution with the cross-functional team
Responsibilities
Lead new Contract Manufacturer (CM) factory bring-up and capacity expansion across global sites Assess CM capabilities to identify opportunities to improve CM operational capabilities and implement operational strategy to inline with product roadmap Drive early-stage readiness across campus planning, Capacity, MFG LT, factory layouts and material flow, labor planning to ensure seamless ramp-to-delivery Oversee the CM execution across production planning, capacity/space utilization, labor efficiency, MES reporting, and factory cost structures to meet key operational SLAs Manage cross-qualification, line conversions for CapEx re-use, and ECN/PRQ execution. Serve as a core member on the Justification Review Board and drive alignment across internal/external cross-functional teams Audit and optimize post-launch FATP and SMT line layouts to streamline material flows, eliminate physical bottlenecks, and drive continuous labor reductions Continuously elevating sustaining operational metrics, including UPH, Overall Equipment Effectiveness (OEE), test cycle time (CT), and retest rate minimization Maintain the functional yield bridge (FPY/LPY) to achieve long-term yield and cost targets. Lead Major Issue List (MIL) and technical priority issue and technical issue forums, directing deep-dive Failure Analysis and Corrective Actions (FACA) for root-cause resolution Leverage scripting/data analytics tools (Python, JMP, Tableau) to automate yield tracking, optimize reporting workflows, and propel Smart Factory initiatives (automation, digital intelligence, and sustainability)
Qualifications
6+ years of experience in high-volume electronics manufacturing, with a focus on operational leadership, sustaining process engineering, or technical program management Deep technical expertise in FATP and SMT processes, including yield bridge maturation, UPH/CT optimization, and OEE enhancement Hands-on experience using data analysis tools (Python, JMP, Tableau, or modern AI tools) to streamline yield metrics, automate reporting, and accelerate FACA cycles Proven track record in project/operations management (Cost, Capacity, Schedule, and line readiness) within dynamic, high-ambiguity environments Exceptional strategic thinking, leadership, and cross-functional influencing skills to hold internal and external stakeholders accountable Ability to travel frequently within the APAC region, with occasional travel to other global sites Experience with automated system bring up and qualification Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated experience in CM site setup, manufacturing transfer, and qualification across different areas/regions Proven interpersonal and communication skills Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Advanced expertise in leveraging modern data intelligence / Smart Factory solutions to improve yield and manufacturing efficiency