Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology. 

What We Offer

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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. 

Traineeship Project Description:

Typically as part of routine maintenance, tools undergo conditioning after a maintenance event. This conditioning typically follows a fixed set of processes which take fixed material and time.

This project aims to enhance the detection and conditioning quality to reduce overall material and maintenance time. The intern will work with process engineers, data scientists and field engineers to analyze sensor data streams, develop and validate algorithms that accurately identify when conditioning is complete, and explore dynamic conditioning methods. The intern will contribute to building data pipelines, running experiments, and refining detection models to improve process consistency and product quality.

Preferred Discipline:

Materials Science & Engineering

Programming in Python; Basic knowledge of machine learning and data analysis; Understanding of physics principles related to sensing and measurement; Familiarity with data visualization tools; Ability to interpret experimental results and draw conclusions. Familiarity with semiconductor manufacturing processes.

Learning Outcome:

The intern will develop hands-on experience in applying machine learning and data analysis techniques to real industrial problems. They will gain exposure to sensor technologies, process engineering workflows, and cross-functional team collaboration. The intern will strengthen their programming skills and build practical knowledge of how applied physics and chemistry principles apply in a manufacturing environment.

Additional Information

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Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.