The SDE team sits at the critical juncture between innovative product design and scalable, high-volume manufacturing - bridging upstream development (product design, material selection) and downstream integration (FATP). You will take ownership of the technical and operational complexities of ramping robotics products from prototype through mass production. This is a unique opportunity to shape how Meta's next-generation robotics hardware gets built at scale. You'll work hands-on with suppliers, develop manufacturing processes, drive quality systems, and collaborate deeply with Hardware, Industrial Design, Process, Sourcing, FATP Operations.
Responsibilities
Manufacturing Process Development: Develop optimized, scalable manufacturing processes for robotics components and modules - from component-level DFM through process bring-up and line qualification at supplier sites
• Quality Engineering: Define quality characterization, inspection methodologies (FAI/CPK, MSA, OQC/IPQC/IQC), and qualification standards; oversee module qualification processes and OK2X execution
• Tooling & Fixture Development: Develop end-to-end process strategies and fixture/process bring-up solutions that provide a clear path to mass production
• Supplier Technical Management: Assess supplier capabilities, equipment readiness, and scalability; contribute to SPOR (Supplier Plan of Record) scorecards and drive vendor capability development
• Design for Manufacturability (DFM): Provide early DFM feedback to Product Design and Industrial Design teams, identify manufacturing process risks before architecture lock, and ensure design intent is preserved through production
• Cross-Functional Collaboration: Partner with Hardware Engineering, Quality, Reliability, TPM, and Strategic Sourcing teams across all phases of the product development lifecycle
• Issue Resolution: Debug manufacturing issues (yield, cosmetics, dimensions) at supplier sites from NPI through ramp; identify root causes and implement corrective actions, apply AI-assisted analysis to identify patterns and support preventive measures
Minimum Qualifications
• BS/MS in Mechanical Engineering, Electrical Engineering, Manufacturing Engineering, Industrial Engineering, Materials Science, or extensive experience in a related field
• 10+ years of experience in supplier development, manufacturing engineering, or process engineering in a hardware product environment
• Hands-on experience with manufacturing processes such as CNC machining, injection molding, die casting, sheet metal fabrication, surface finishing (painting, anodization, coating), or PCBA assembly
• Demonstrated ability to drive DFM, process optimization, and quality improvement at supplier sites
• Experience with GD&T, statistical process control (SPC/CPK), and measurement system analysis (MSA)
• Strong understanding of NPI lifecycle from prototype (EVT/DVT/PVT) through mass production ramp
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience with failure analysis and troubleshooting cosmetic and functional coating defects
• Experience in managing multiple suppliers across and/or multiple projects
• Willingness to travel domestically and internationally (up to 25-40%) to supplier sites
Preferred Qualifications
• Experience managing multi-supplier programs and driving process standardization across vendors
• Experience in robotics, consumer electronics, automotive, or medical device manufacturing
• Strong project management skills with ability to manage multiple concurrent NPI programs
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Knowledge of advanced surface finishing processes (CMF, softgoods, functional coatings)
• Familiarity with electromechanical assemblies (motors, actuators, cable assemblies)
• Background in supplier qualification, audit, and capability assessment (supplier scorecard evaluation)
• Proficiency with metrology equipment and inspection methodologies (CMM, optical measurement, CT scanning)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience with FMEA, 8D, and root cause analysis methodologies
