Physics AI Scientist III

Applied Materials•Santa Clara, CA

1d•Onsite

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. 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.

Requirements

Experience applying machine learning to real - world scientific or engineering problems.
Strong background in scientific machine learning and numerical simulation.
Proficiency in Python and PyTorch.
Experience with HPC, distributed training, large-scale scientific datasets or scalable ML workflows.

Nice To Haves

Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, Computer Science, or a related field.
Experience with surrogate modeling, PINNs, operator learning (e.g., FNO), or foundation models.

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.