Drimva

Intel

Ocotillo Technology Fabrication Defect Metrology Equipment Engineer

$89K–$171K

US, Arizona, Phoenix · Full-time · Posted Oct 16, 2026

Experience
Minimum: 3+ months with Yield and Defect Metrology equipment, plus 2+ years of relevant experience with a bachelor’s degree or 1+ years with a master’s degree. Preferred: includes 3+ years of New Technology startup experience and 3+ years of relevant Process/Yield Engineering experience.
Education
Bachelor’s degree in a listed or related field, or master’s degree in a listed or related field.

About the job

Optimizes and maintains defect metrology equipment and data systems to improve yield, product integrity, and manufacturing performance at leading-edge logic fabs.

What you'll do

  • Analyze process, defect, electrical, and manufacturing data to identify yield limiters and guide process improvements.
  • Harden metrology equipment systems and support new product introductions and design of experiments.
  • Develop AI/ML applications and tools for patterning solutions, design efficiency, and high-volume data analysis.

What they ask for

  • At least 3 months of experience with Yield and Defect Metrology equipment.
  • Bachelor’s degree in a listed or related field and 2+ years of relevant experience, or a master’s degree in a listed or related field and 1+ years of relevant experience.
  • Degree field must be Electrical Engineering, Microelectronics Engineering, Material Science Engineering, Chemical Engineering, Mechanical Engineering, Optical Engineering, Statistics/Data Science, Chemistry, Physics, or a related field.

Nice to have

  • Advanced degree and 2+ years of applications experience with defect metrology tools.
  • Semiconductor fabrication knowledge and experience with statistical analysis or programming, such as JMP/JSL, Python, R, or SQL.

Pay

$89,010.00-170,630.00 USD annual target compensation (base salary or total pay not specified)

Tools

KLA, AMAT, ASML HMI, TEL, Keysight, Advantest, Hitachi, Python

AI focus

Develop AI/ML applications to improve patterning solutions and design efficiency.

Model-written summary · the original posting is authoritative

First seen Oct 17, 2026 · Last verified Nov 5, 2026 (UTC)