Image Processing & ML Intern - Chip Inspection

KLA; Milpitas (CA); On-site; USD 68,000 - 80,000; Full time

Job summary

KLA is seeking enthusiastic master's or Ph.D. students to tackle cutting-edge projects in our engineering and scientific teams in Milpitas, CA. Join to apply image analysis and machine learning to semiconductor inspection, contributing to process control and yield management solutions.

This role offers hands-on experience in a dynamic environment, with opportunities to develop methods, benchmarks, and reproducible analyses while collaborating with researchers and engineers across groups.

Qualifications

• Currently pursuing a Master's or Ph.D. in mathematics, statistics, physics, engineering, computer science, materials science, biology, geography or a related field.
• Relevant coursework or projects in image processing, computer vision, or ML, not necessarily image-focused.
• Strong Python programming skills.
• Solid foundation in statistics.
• Strong analytical and problem-solving skills with curiosity to learn new methods.

Responsibilities

• Investigate image processing, computer vision, and machine learning methods for semiconductor inspection.
• Implement, adapt, and evaluate methods on optical or SEM datasets.
• Develop reproducible analyses and benchmarks to compare methods and understand strengths and limitations.
• Collaborate with scientists and engineers to interpret results and inform future R&D.
• Document methods and findings, and present results and recommendations to the team.

Skills

Python programming

Statistics

Analytical thinking

Independent working

Collaborative work

Education

Master's or Ph.D. in a related field

Image processing / machine learning coursework

Job description

KLA is seeking enthusiastic master's or Ph.D. students to tackle cutting-edge projects in our engineering and scientific teams in Milpitas, CA. Join to apply image analysis and machine learning to semiconductor inspection, contributing to process control and yield management solutions.

This role offers hands-on experience in a dynamic environment, with opportunities to develop methods, benchmarks, and reproducible analyses while collaborating with researchers and engineers across groups.