Physics AI Scientist

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 worlds technology.

What We Offer

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Youll 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 possiblewhile learning every day in a supportive leading global company. Visit our Careers website to learn more.

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Role Summary

Develop next-generation AI models for semiconductor engineering by combining scientific domain knowledge with modern machine learning. Build physics-aware surrogate models foundation models and optimization workflows that accelerate simulation and engineering design.

Key Responsibilities

• Develop Scientific AI models that bridge scientific computing physics-based simulation and machine learning for physics and chemistry-based engineering problems.
• Design and train surrogate models operator-learning models and foundation models for scientific simulations.
• Collaborate with domain experts to formulate AI solutions for complex engineering challenges.
• Develop scalable data generation training validation and deployment workflows for Scientific AI models.
• Publish technical innovations and drive adoption of Scientific AI across engineering applications.

Preferred Qualifications

• Ph.D. in Mechanical Engineering Chemical Engineering Materials Science Physics Applied Mathematics Computer Science or a related field.
• Strong background in scientific machine learning and numerical simulation.
• Experience with surrogate modeling PINNs operator learning (e.g. FNO) or foundation models.
• Proficiency in Python and PyTorch.
• Experience with HPC distributed training large-scale scientific datasets or scalable ML workflows.

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.