Physics AI Scientist III

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.

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.

• Experience applying machine learning to real-world scientific or engineering problems.

• 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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The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.

For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.

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.

In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at [email protected],or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.