Data Science / Optimization Intern

Applied Materials • Austin, CA

12h

About The Position

We are seeking highly motivated Data Science / Optimization Interns to work on AI‑driven recipe and hardware optimization problems in semiconductor process applications. The role focuses on developing and applying machine learning and optimization techniques using physics‑informed and data‑driven surrogate models , with mentorship and training provided by experienced engineers and data scientists.

Requirements

• Currently pursuing a Bachelor’s degree in: Computer Science Data Science Electrical, Mechanical, or Chemical Engineering Applied Mathematics or a related technical field
• Strong programming skills in Python
• Understanding of machine learning fundamentals
• Coursework or hands‑on experience in optimization, numerical methods, or scientific computing
• Ability to work with data, debug models, and learn quickly

Nice To Haves

• Exposure to optimization techniques (e.g., gradient‑based methods, Bayesian optimization)
• Experience working with simulation or experimental data
• Familiarity with NumPy, SciPy, scikit‑learn, or PyTorch
• Interest in applied engineering or manufacturing problems

Responsibilities

• Develop and apply machine learning models for surrogate modeling of physical and engineering systems
• Support optimization algorithms for recipe and hardware parameter tuning
• Analyze simulation and experimental data to improve model accuracy and performance
• Build Python‑based workflows for model training, inference, and evaluation
• Collaborate with engineers and scientists to translate engineering problems into data‑driven models
• Document methods and results and present findings to technical stakeholders

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Number of Employees: 11-50 employees