Manufacturing Design Engineer

The RL (Reality Labs) Team at Meta is helping more people around the world come together and connect through hardware and software. As part of the RL Devices team, we are builders who break down barriers to what is possible. Together we design and build manufacturing processes that transform technology into products. The Manufacturing Design Engineer team has the responsibility for evaluating technologies and identifying supply chain solutions that allow Meta to deliver products to market at scale. We are seeking a Manufacturing Design Engineer (Manufacturing Design Engineer) able to work independently to drive manufacturing solutions from concept through production. Specific hardware development and operations knowledge are required. As a Manufacturing Design Engineer (MDE) in the Devices organization, you will be responsible for both technically proficient work with the engineering design and Operations teams, for critical parts of optical and mechanical components process development and manufacturing at vendors. You will collaborate across multiple projects with optical and mechanical product designers to enable novel Smart Glass, AR/VR experiences. You will be the interface between the Tech Ops Engineering, Supply Chain, and Manufacturing teams to implement and execute comprehensive process and technical solutions for our products. Drive manufacturing process innovation in mechanical and optical component solutions for Smart Devices and AR/VR products Work with Hardware Engineering, Sourcing, Quality, and Tech Ops teams to identify and audit and select the best suppliers’ partner (SPOR) to manufacture our products Responsible for manufacturing processes (molding, forging, assembly, materials, surface finishing), cover window manufacturing (i.e. optical coatings, painting), film/lens lamination, die cut, laser cutting, adhesion (i.e. Optical Crystal Adhesion) Process DFM, the process brings up and qualifications Develop an MP intent, equipment, process, and materials Set CTO criteria to validate key processes Run statistical analysis from the data collected through trials and investigations Work with the internal Quality Team to define quality characterization, and inspection methodology, and qualification of components before components ship to downstream vendors Work with hardware engineering, quality, and reliability teams to debug mechanical/optical issues related to component development from NPI to ramp. Identify root cause and provide improvements via process optimization and qualifications Strong leadership in a program or operation supports, and being able to multitask when needed Travel to vendor sites as needed to validate processes and support project development BS degree or higher in Mechanical Engineering, Materials Science, Chemistry, Physics, or equivalent experience 10+ years of technical hands-on experience with mechanical, display, camera, or optical manufacturing Experience partnering with multiple cross functional partners Experience with consumer electronics manufacturing and/or highly cosmetic parts Familiar with statistical analysis from the data gathered Familiar with quality engineer works, such as defect characterization, yield summary report and driving yield bridge FACA activities 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 Experience with multiple surface finishing and coating methods. Optical coatings, paintings, PVD, CVD, Plating, Anodizing, Electro Deposition, etc Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience working with a variety of materials, including plastics, metals, and glass 15+ years of manufacturing, surface finish and optical process development experience Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)