Director, AI Manufacturing Enablement

Meta's Manufacturing Engineering & Operations (MEO) organization is responsible for bringing Reality Labs' most ambitious hardware products to life at scale. Across our functions — Manufacturing Operations, DFx, Manufacturing Test Engineering, and Quality — hundreds of workflows involve data analysis, decision-making, documentation, and cross-functional coordination that are ripe for AI-driven transformation. We are seeking a Director, AI Manufacturing Enablement to lead a dedicated team that systematically identifies, prioritizes, and deploys AI and automation solutions across the MEO organization and its adjacent partners. This leader will operate at the intersection of manufacturing domain expertise and applied AI, delivering measurable productivity gains, quality improvements, and workflow automation across real production environments. This is a founding leadership role reporting to the Director of MEO. You will build the team, define the AI strategy for manufacturing, and serve as the connective tissue between Meta's broad AI capabilities and the practical realities of hardware manufacturing.

Responsibilities

• Conduct systematic workflow audits across all MEO functions to identify high-value AI/automation opportunities — categorized by efficiency gain, quality improvement, or full automation potential
• Develop and maintain an AI opportunity roadmap prioritized by impact, feasibility, and strategic alignment
• Scout adjacent organizations (SW Engineering, Data Science, XRT, Production Engineering) for existing tools, models, and frameworks that can be adapted for manufacturing use cases
• Stay current on Meta's internal AI platform capabilities (LLMs, computer vision, anomaly detection, generative AI) and translate them into manufacturing applications
• Build and ship AI-powered tools for MEO workflows including automated defect classification, AI-assisted DFx review, predictive yield modeling, intelligent test sequencing, natural language interfaces for manufacturing data queries, and automated reporting and documentation
• Define success metrics for each deployment (time saved, error reduction, decision quality improvement) and own the end-to-end lifecycle from problem definition through production deployment and iteration
• Build partnerships with Meta's AI/ML infrastructure teams to leverage internal platforms and partner with Manufacturing Test Engineering on AI-enabled test optimization
• Collaborate with contract manufacturers on AI readiness, data infrastructure, and tool deployment at factory sites
• Recruit, hire, and develop a team of applied AI/ML engineers and manufacturing-domain technologists
• Build a team culture that values rapid prototyping, user-centric design, and measurable impact
• Establish frameworks for evaluating build-vs-buy-vs-adapt decisions for AI tools

Minimum Qualifications

• 12+ years of experience across manufacturing/operations and applied AI/ML/automation, with demonstrated depth in at least one and working fluency in the other, including at least 5 years in a leadership role
• Experience deploying AI/ML solutions in production environments, or diverse leadership experience leading complex operations through changes involving the introduction of technology to improve productivity and quality of decision making
• Understanding of manufacturing workflows — assembly, test, quality, supply chain — and where AI adds genuine value
• Experience building and leading technical teams that ship products/tools iteratively
• Experience operating in ambiguity — defining the problem space, not just solving well-scoped problems
• Experience in process transformation and organizational change management — driving adoption of new tools and ways of working across engineering teams
• Bachelor's degree in Computer Science, Industrial Engineering, Manufacturing Engineering, or a related technical field, or equivalent practical experience demonstrated through a track record of leading process transformation, automation, or applied technology deployment in a manufacturing or operations environment Experience with manufacturing data systems (MES, SPC, historian databases) and the data quality challenges they present
• Experience in consumer electronics or precision hardware manufacturing environments
• Familiarity with large-scale AI/ML platforms or similar large-scale AI platforms
• Hands-on familiarity with LLMs, computer vision, time-series anomaly detection, or reinforcement learning in industrial settings
• Track record of building new functions or teams from zero — particularly 'AI for X' teams embedded in non-AI organizations