Industrial AI Equipment Engineer

Industrial AI Equipment Engineer

ONSEMI  |  GRESHAM CAMPUS

Equipment Engineering • Manufacturing Equipment Maintenance

As Industrial AI Equipment Engineer at onsemi's 200 mm Gresham fab, you will develop and deploy practical AI, machine-learning, and advanced-analytics solutions that improve equipment availability, troubleshooting effectiveness, maintenance knowledge retention, and manufacturing decision-making.

You will own the equipment AI roadmap in partnership with equipment engineers and technicians, and then execute on it – spanning physical sensor and vision integrations at the tool, AMR and automation deployments, and digital AI/ML platforms built on fab and facilities data. The role combines controls, IT/OT, and data-integration expertise with the systems-engineering discipline to move ideas from proof-of-concept to sustained production value.

• Develop and own the equipment AI roadmap in partnership with equipment engineers, technicians, process, facilities, and MOS/IT – then prioritize and execute against measurable equipment and manufacturing outcomes.
• Design, build, and deploy practical AI/ML and advanced-analytics solutions from proof-of-concept through sustained production.
• Integrate sensors, vision systems, and controls at the tool – specifying and standing up data collection, IT/OT interfaces, and networking while maintaining fab security and reliability requirements.
• Lead automation and AMR deployments, managing interfaces from IoT/sensor devices through equipment-control software.
• Build the data foundation – connect equipment (XMA/XSITE), FDC/FabGuard, MES, and Ignition/SCADA facilities data into trusted, reusable datasets, digital-twin, and data-lake structures.
• Develop AI agents and knowledge assistants that capture maintenance procedures, BKMs, OEM manuals, and schematics so junior technicians can resolve issues faster as senior technicians retire.
• Partner with corporate AI & Automation, MOS/IT, and vendors to clear obstacles (access, licensing, platform selection) and align with responsible-AI, data-privacy, and security standards.
• Mentor engineers and technicians on data and AI methods, document solutions clearly, and drive adoption so value is retained and does not depend on tribal knowledge.

• Bachelor’s degree in Electrical Engineering, Systems Engineering, Computer Science, or a related field; advanced degree preferred.
• 10+ years of controls, automation, or equipment engineering experience in a manufacturing environment, including hands-on work on automated production equipment.
• Demonstrated PLC and controls expertise, including vision-guided systems and robot-handling equipment.
• Proven IT/OT and data-integration experience, including data collection and MES integration in a manufacturing environment.
• Software development capability with data-acquisition and dashboarding experience.
• Ability to lead projects independently from concept through sustained deployment and to communicate clearly across engineering, manufacturing, and leadership.

• Direct semiconductor equipment experience (e.g., Novellus, Applied Materials, or comparable 200 mm platforms) and familiarity with chemical and gas delivery systems.
• Hands-on machine-learning and applied-AI experience – image/defect classification, predictive maintenance, clustering, or LLM-based agents built on internal documents and equipment history.
• Experience deploying autonomous mobile robots (AMR) and managing the full stack from sensor/IoT devices to management software (e.g., MiR training or equivalent).
• Systems-engineering background with data-modeling/simulation and the ability to architect data lakes and digital twins across fab and facilities data.
• Experience leading a plant-wide “data collection by design” or automation-transformation program and interfacing between IT and manufacturing.
• 3D CAD and printing skills for custom sensor mounting and integration.
• Lean/Six Sigma exposure and a track record of reducing dependence on tribal knowledge through documentation and standardization.