Job description

At Meta, we're building the future of human connection and the technology that enables it. This means continuously inventing and developing technologies for the next generation of experiences. To continue our efforts in the path to AGI, and as we move closer to a future with intelligent robots and advanced AI models, we're hiring talent across a broad range of disciplines from robotics hardware to system software, machine perception, and artificial intelligence. These crucial projects and initiatives taken on by this team have never been done before, so you have a rare opportunity to help us create new ways people connect around the world. The Meta Robotics Hardware team is seeking a Mechanical Design Engineer focused on sustaining engineering to own the design health and continuous improvement of our humanoid robot hardware after initial release. You will be the primary engineering contact for failure investigation, corrective design action, and design-for-manufacture improvements driven by real-world feedback from our fabrication shops and assembly teams. This is a hands-on role that lives at the boundary between the design office and the shop floor — you'll spend as much time reviewing failed parts and assembly pain points as you do in CAD. This role will be part of a ME team responsible for concept development, analytical modeling, prototype design, first article build, fabrication, vendor management, and testing. Candidates should be comfortable working in a high ambiguity, fast paced environment where full system requirements are seldom known. This team is dedicated to building hardware that enables learning through data collection and system testing. Lead root-cause analysis (RCA) on mechanical failures — fractured components, worn interfaces, fatigue cracks, fastener loosening, tolerance stack-up issues Own the corrective action process: document failure mode, determine root cause, implement and verify design fix, and update drawings and specifications Maintain a failure tracking database; identify recurring failure patterns and drive systemic design improvements Collaborate with test and reliability engineers to correlate field/lab failures back to design margins and loading assumptions Initiate and manage Engineering Change Orders (ECOs) to implement corrective actions into production documentation Serve as the primary design engineering liaison to the assembly and integration team Collect, triage, and action assembly feedback — difficult-to-reach fasteners, unclear build sequences, interference issues, tooling access constraints, and handling concerns Implement design changes that reduce assembly time, lower operator skill requirements, and improve build repeatability Develop and revise assembly work instructions, torque specifications, and inspection criteria based on build technician input Participate in hands-on build activities to first-hand identify ergonomic and process pain points Design tooling to facilitate complex component assembly Maintain and improve the sustaining engineering change process — from issue identification through design release and verification Support configuration management and BOM accuracy for all active robot builds Track and report sustaining metrics: open issues, mean time to corrective action, repeat failure rate, assembly hours per unit Develop and document standard work instructions and assembly aids for prototype and production builds Work closely with a cross-functional team of engineers (Electrical, Software, Firmware) to deliver complete technical solutions Troubleshoot root-cause of mechanical failures, implement improved designs, and test effectiveness of changes BS degree in Mechanical Engineering 7+ years of experience in Mechanical Design Engineering with a meaningful portion in sustaining, production, or manufacturing engineering Strong CAD proficiency (SolidWorks, NX, or CREO) including full drawing packages with GD&T per ASME Y14.5 Working knowledge of CNC machining processes — understanding of fixturing, tool access, setup minimization, and achievable tolerances Experience interpreting assembly feedback and translating it into actionable design improvements Hands-on experience working on a shop floor or in a build area Familiarity with engineering change management processes (ECO/ECN/ECR workflows) Experience with rapid prototyping manufacturing processes and materials Experience managing time-sensitive projects through to completion while balancing evolving priorities and a broad range of stakeholders Experience in tolerance analysis and geometric dimensioning and tolerancing (GD&T per ASME Y14.5) Experience in Finite Element Analysis (FEA) Experience with tolerance analysis tools and methodologies (RSS, Monte Carlo, worst-case) Familiarity with common joining and fastening methods and their trade-offs in robotic assemblies Experience communicating technical recommendations and influencing design decisions across multidisciplinary teams (e.g., failure reports, design reviews, ECO proposals) Experience with lightweighting strategies (topology optimization, lattice structures, composite materials) Background in high-mix/low-volume transitioning to mid-volume production environments Exposure to Lean manufacturing principles or structured problem-solving methodologies (8D, A3, Ishikawa) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with additional manufacturing processes: sheet metal, additive manufacturing, anodizing, and plating Ability to read and interpret FEA results to inform failure investigations and design margin assessments Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Direct experience with robotic systems — BLDC motors, actuators, gearboxes, linkage mechanisms Experience writing clear failure reports that cross-functional teams can act on Experience with PLM/PDM systems (Teamcenter, SolidWorks PDM, Windchill, or similar) Familiarity with statistical tolerance analysis and process capability (Cp/Cpk)

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Platform: meta
Effort: medium