Machine Learning Engineer Intern - Physics-Informed Imaging

KLA; Milpitas (CA); In loco; USD 68.000 - 80.000; Tempo pieno

Vantaggi offerti da questo lavoro

Medical benefits

Dental benefits

Vision benefits

401(k) matching

Descrizione del lavoro

KLA invites an ML Engineer Intern to join our algorithms team in Milpitas, CA, applying machine learning to semiconductor manufacturing. You’ll work with experienced engineers to develop ML solutions for real-world data, advancing CD metrology and physics-informed modeling.

Responsibilities include building models, exploring limited-data strategies, and presenting results. Strong Python and PyTorch skills and a STEM background are required, with a commitment to hands-on learning.

Competenze

• Currently pursuing a Master’s or PhD in Computer Science, Electrical Engineering, Physics, Applied Mathematics or a related STEM field.
• Strong Python and hands‑on deep learning experience (PyTorch or similar).
• Solid ML and data science fundamentals and strong problem‑solving skills.

Mansioni

• Build, train and evaluate ML and deep learning models for critical dimension (CD) metrology.
• Develop physics-informed ML approaches that bring physical models and constraints into model design and training.
• Explore methods for learning from limited data and estimating uncertainty.
• Write clean, reusable Python code and present your results to the team.

Conoscenze

Python

Deep learning

PyTorch

Problem solving

Formazione

Master’s or PhD in CS/EE/Physics/Applied Mathematics

Strumenti

PyTorch

Descrizione del lavoro

KLA invites an ML Engineer Intern to join our algorithms team in Milpitas, CA, applying machine learning to semiconductor manufacturing. You’ll work with experienced engineers to develop ML solutions for real-world data, advancing CD metrology and physics-informed modeling.

Responsibilities include building models, exploring limited-data strategies, and presenting results. Strong Python and PyTorch skills and a STEM background are required, with a commitment to hands-on learning.