总监，AI制造赋能

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

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

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 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

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

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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.