Director, Physical AI, Smai

We are hiring a Director for Physical AI to integrate robotics, physical automation, and AI-driven control logic into Micron's smart manufacturing ecosystem -- enabling autonomous, adaptive, and highly efficient manufacturing operations at scale. You will drive cross-functional transformation, partnering with site and global manufacturing and engineering teams to embed physical AI capabilities seamlessly into production environments. Job Responsibilities Lead Micron's Physical AI strategy and delivery across FE and AT manufacturing, including: - Define and lead Micron's Physical AI vision and roadmap across FE and AT manufacturing, and identify high-impact use cases (material handling, smart dispatching, adaptive automation) Establish a scalable framework for robotics integration, orchestration, and control logic within Micron's smart manufacturing systems Drive integration of robotics platforms, AMRs, tool automation, and control systems with SMAI applications and the Micron MES ecosystem Architect solutions that combine AI/ML models, decision engines, and robotics execution layers, ensuring interoperability across heterogeneous systems and hardware vendors Lead end-to-end deployment from pilot to high-volume manufacturing (HVM) across global FE/AT sites Establish best practices for standardisation, scalability, and reliability of physical AI solutions; drive adoption and value realisation across sites Partner with site leadership, central engineering, and supply chain stakeholders to align priorities, translate manufacturing needs into AI/automation solutions, and influence global investment prioritisation Stay at the forefront of robotics, AI, and autonomous systems innovation; drive experimentation with AMRs, digital twins for physical systems, real-time optimisation, and closed-loop control Co-develop with external partners, vendors, and research organisations on emerging robotics and AI capabilities; represent Micron in joint roadmaps Establish KPIs and report quarterly to executive leadership on Physical AI impact across productivity, cost, cycle time, safety, and inventory across global sites Work with AI developers team to generate agentic AI workflow where applicable Lead People and Teams Build, mentor, and lead a high-performing, multi-disciplinary team spanning data science, robotics, software, and automation engineering Act as a key interface between technical teams and business leadership, translating manufacturing needs into AI and automation solutions Exceptional stakeholder management and influencing skills across global organisations; comfortable leading in ambiguous, fast-evolving environments Apply strong systems-thinking and problem-solving capability to deep automation architecture and manufacturing systems decisions Champion AI literacy and drive organisational change management so fab operators, technicians, and engineers trust and adopt AI-driven robotics Demonstrate accountability and lead with strategic thinking, strong execution discipline, passion, and integrity Qualifications Bachelor's degree in Engineering, Computer Science, Robotics, or related field (Master's or MBA a strong plus) 10+ years of experience in manufacturing, automation, robotics, or AI systems, with proven leadership in large-scale cross-functional programmes Experience in semiconductor manufacturing (FE and/or AT) environments Strong background in robotics integration (AMR, AGV, tool automation) and smart manufacturing systems (MES, automation platforms) Proven track record deploying AI/ML in operational settings, with demonstrated ability to translate complex technical concepts into business impact Deep understanding of manufacturing systems and automation architecture; exceptional influencing skills across global, matrixed organisations In new product design roles: Provides technical leadership and oversight of the development of integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments. For product/system performance projects: Directs the use of machine language and statistical modeling techniques such as decision trees, logistic regression and Bayesian analysis to develop and evaluate algorithms to improve performance, quality, data management and accuracy. Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations. Adapts machine learning to areas such …