Data Science / Optimization Intern

Applied Materials • Austin, TX

2d

About The Position

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.
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: 5,001-10,000 employees