Factory APC Engineer: R2R Control & ML-Driven Optimization

Texas Instruments; Sherman (TX); On-site; USD 95,000 - 150,000; Full time

Job summary

Texas Instruments is seeking a Factory Controls Engineer (APC) to join the team launching a new 300mm fab and driving continual improvement. You will work with Fab Engineering, process, equipment automation, data engineering and data science to develop APC solutions and predictive methods that reduce downtime and improve repeatability.

The role emphasizes APC development, MES familiarity, DOE/SPC, and integrating machine learning with traditional control to deliver world-class quality and

Responsibilities

• Support APC R2R processes to accelerate equipment/startup and SWRs.
• Support the ramp of a new factory with equipment installation, startup and release from a process perspective.
• Accelerate R2R control development: new features, APC tools, algorithms, and ML integration.
• Learn and operate multiple manufacturing execution systems (MES) quickly and proficiently.
• Support high-volume 300mm Fab operations in Analog or Embedded Processing Technologies.
• Develop and implement improved manufacturing procedures to boost efficiency, yields, and quality.
• Execute improvement plans for efficiency, cost reduction, or quality programs.
• Identify systematic problems in processes or equipment and communicate findings to engineering.
• Participate in data analysis and problem resolution.
• Develop new processes for existing or future technologies with high performance and low cost.
• Apply statistical rigor through SPC, DOE, modeling and other advanced data analysis methods.

Job description

Texas Instruments is seeking a Factory Controls Engineer (APC) to join the team launching a new 300mm fab and driving continual improvement. You will work with Fab Engineering, process, equipment automation, data engineering and data science to develop APC solutions and predictive methods that reduce downtime and improve repeatability.

The role emphasizes APC development, MES familiarity, DOE/SPC, and integrating machine learning with traditional control to deliver world-class quality and