Image Processing Intern — ML for Chip Inspection

New Grad

Milpitas (CA)

On-site

USD 68,000 - 80,000

Part time

3 days ago
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Job summary

KLA is inviting you to join our Semiconductor Products and Customers group in Milpitas as a student contributor. You’ll work alongside engineers and scientists on image analysis and machine learning applied to semiconductor inspection, gaining hands-on experience in a dynamic tech environment.

We’re seeking master's or Ph.D. students with strong Python skills, solid statistics foundation, and a curious, collaborative mindset.

Qualifications

  • Currently pursuing a Master’s or Ph.D. degree in a related field (maths, stats, physics, engineering, CS, materials, biology, geography).
  • Skills in image processing, computer vision, machine learning or deep learning; strong Python programming; solid statistics foundation.

Responsibilities

  • Investigate image processing, computer vision, and machine learning methods relevant to semiconductor inspection.
  • Implement, adapt, and evaluate methods on optical and/or scanning electron microscope image datasets.
  • Develop reproducible analyses and benchmarks to compare methods and understand strengths, limitations, and data suitability.
  • Collaborate with scientists and engineers to interpret results and inform future research and development.
  • Document methods and findings, and present results and recommendations to the team.

Skills

Python programming
Statistics
Analytical thinking
Communication
Independent work

Education

Master’s or Ph.D. degree in mathematics, statistics, physics, engineering, computer science, materials science, biology, geography or a related field

Tools

Python

Job description

KLA is inviting you to join our Semiconductor Products and Customers group in Milpitas as a student contributor. You’ll work alongside engineers and scientists on image analysis and machine learning applied to semiconductor inspection, gaining hands-on experience in a dynamic tech environment.

We’re seeking master's or Ph.D. students with strong Python skills, solid statistics foundation, and a curious, collaborative mindset.

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