Applied Materials is a global leader in materials engineering solutions for the electronics industry. They are seeking highly motivated Data Science / Optimization Interns to work on AI-driven optimization problems in semiconductor processes, focusing on machine learning and optimization techniques.
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
Qualification
Required
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
Preferred
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
Benefits
Comprehensive benefits package
Participation in a bonus and a stock award program
Applied Materials is a semiconductor and display equipment company that offers materials engineering solutions.
