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SM Fab Fault Detection; FD) Engineer

Job in Sherman, Grayson County, Texas, 75091, USA
Listing for: Texas Instruments
Full Time position
Listed on 2026-10-16
Job specializations:
  • Engineering
    Process Engineer, Manufacturing Engineer, Automation & Mechatronics Engineer, Quality Engineering
Salary/Wage Range or Industry Benchmark: 90000 - 120000 USD Yearly USD 90000.00 120000.00 YEAR
Job Description & How to Apply Below

Change the world. Love your job.

We can't predict what the future holds, but we know Texas Instruments will have a part in shaping it.

Texas Instruments is looking for an energetic engineer with a passion for process control and automation. This position provides the opportunity to start up a new factory and drive continual improvement at all levels. In this position, you’ll have the opportunity to collaborate with process engineering, equipment automation, data engineering, and data science teams to develop innovative solutions to reduce unscheduled tool downtime, improve process repeatability/reliability, and protect the factory from wafer-loss events.

The

responsibilities of a Factory Controls Engineer (FD) in this role include:
  • Support manufacturing operations in a high-volume manufacturing 300mm wafer Fab running Analog
    Technologies
  • Support the manufacturing ramp with execution of FDC models that accelerate equipment/process
    startup and process experiments
  • Develop and implement improved equipment fault detection (FDC) models to prevent excursions and
    scrap events
  • Identify systemic problems in either process or equipment related sources and communicate
    findings to the engineering staff in a continuous manner
  • Participate in data analysis and problem resolution
  • Learn and execute in multiple manufacturing execution systems (MES) quickly and proficiently
  • Implement project or improvement plans for areas of responsibility including but not limited to: efficiency improvements, cost reduction or quality improvement programs, new product support and other process improvements
  • Apply statistical rigor through SPC modeling and other advanced data analysis methods
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