Drimva

Micron

Senior Manager, Wet Process Equipment Engineering

Boise, ID - Main Site · Manager · Full-time · Posted Oct 13, 2026

Experience
10+ years of semiconductor manufacturing experience and 5+ years of leadership experience.
Education
Bachelor’s degree or higher in Engineering, Chemical Engineering, Materials Science, Physics, Chemistry, or a related technical field.

About the job

Leads Micron’s wet process equipment engineering organization and strategy to improve equipment reliability and high-volume semiconductor manufacturing performance.

What you'll do

  • Lead and develop engineering leaders, equipment engineers, and technical experts.
  • Own wet process equipment strategy and improve reliability, availability, throughput, quality, cost, and maintenance performance.
  • Partner across engineering, manufacturing, facilities, suppliers, and global teams on technology introductions, factory output, capacity plans, and capital projects.

What they ask for

  • Bachelor’s degree or higher in Engineering, Chemical Engineering, Materials Science, Physics, Chemistry, or a related technical field.
  • 10+ years of semiconductor manufacturing experience, including equipment engineering, reliability, maintenance strategy, and performance optimization.
  • 5+ years of leadership experience managing engineering teams, technical organizations, or engineering managers.
  • Demonstrated success leading complex cross-functional technical initiatives and delivering measurable operational improvements in high-volume manufacturing.

Nice to have

  • Experience with wet processing equipment, chemical delivery systems, contamination control, or defectivity reduction.
  • Experience with 300mm manufacturing, factory ramp, predictive maintenance, advanced process control, Lean Manufacturing, Six Sigma, or AI-enabled analytics.

Tools

AI-assisted analytics, advanced analytics, automation, digital manufacturing solutions, advanced process control, Lean Manufacturing, Six Sigma

AI focus

Preferred experience using AI-assisted analytics and AI-enabled tools for data interpretation, anomaly detection, yield improvement, equipment performance, operational effectiveness, and engineering productivity.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)