Thin-Film Process Lead — AI-Powered Shift Ops

1000 Micron Technology, Inc.; Boise (ID); On-site; USD 110,000 - 140,000; Full time

Benefits offered by this job

Medical plans

Dental plans

Vision plans

Paid time off

Paid holidays

Job summary

Micron Technology, Inc. is seeking a Films Manufacturing Development Engineer lead in Boise to drive thin films process development and wafer flow, while coaching engineers across shifts and removing barriers to progress.

You will partner with process, integration, and equipment teams to align priorities, ensure data quality, and apply data science and AI-driven approaches to improve yields and productivity.

Qualifications

• Bachelor's degree in engineering or related field or equivalent experience.
• Experience in thin films process development including CVD, PVD, or related areas.
• Demonstrated leadership in a technical or manufacturing environment.
• Strong problem-solving and communication skills.
• Understanding of deposition physics, kinetics, or transport phenomena.

Responsibilities

• Lead shift-based execution for thin films process development and wafer flow.
• Manage and develop engineers through coaching, mentorship, and performance feedback.
• Partner with process, integration, and equipment teams to align priorities.
• Ensure data quality, address customer concerns, and resolve yield and defect challenges.
• Drive operational improvements using data science and AI-Enabled approaches.

Skills

Thin films process development

CVD

PVD

Leadership

Problem-solving

Communication

Deposition physics/kinetics/transport

Data science/AI in engineering

Education

Bachelor's degree in engineering or related field

PhD or MS or equivalent with 5+ years, or BS with 10+ years of semiconductor experience

Job description

Micron Technology, Inc. is seeking a Films Manufacturing Development Engineer lead in Boise to drive thin films process development and wafer flow, while coaching engineers across shifts and removing barriers to progress.

You will partner with process, integration, and equipment teams to align priorities, ensure data quality, and apply data science and AI-driven approaches to improve yields and productivity.