Industrial AI Equipment Engineer
ONSEMI | GRESHAM CAMPUS
Equipment Engineering • Manufacturing Equipment Maintenance
Position Summary
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.
onsemi (Nasdaq: ON) is driving disruptive innovations to help build a better future. With a focus on automotive and industrial end-markets, the company is accelerating change in megatrends such as vehicle electrification and safety, sustainable energy grids, industrial automation, and 5G and cloud infrastructure. With a highly differentiated and innovative product portfolio, onsemi creates intelligent power and sensing technologies that solve the world's most complex challenges and leads the way in creating a safer, cleaner, and smarter world.
More details about our company benefits can be found here:
https://www.onsemi.com/careers/career-benefits
Minimum Qualifications
- 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.
Preferred Qualifications
- 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.
Key Responsibilities
- 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.
Key Responsibilities
- 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.