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 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.

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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.