Senior Physics AI Scientist for Semiconductors

Aimlroles; California; On-site; USD 161,000 - 221,000; Full time

Job summary

Applied Materials is seeking a Scientist/Engineer to develop next-generation AI models for semiconductor engineering, combining domain knowledge with modern machine learning. You will build physics-aware surrogate models, foundation models, and optimization workflows to accelerate simulation and design.

Collaborate with domain experts to translate complex engineering challenges into scalable AI solutions, and contribute to scalable data generation, training, and deployment across engineering

Qualifications

• PhD required in a relevant 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.

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.

Skills

Python

PyTorch

HPC

Distributed training

Surrogate modeling

PINNs

Foundation models

Education

Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, Computer Science, or a related field

Job description

Applied Materials is seeking a Scientist/Engineer to develop next-generation AI models for semiconductor engineering, combining domain knowledge with modern machine learning. You will build physics-aware surrogate models, foundation models, and optimization workflows to accelerate simulation and design.

Collaborate with domain experts to translate complex engineering challenges into scalable AI solutions, and contribute to scalable data generation, training, and deployment across engineering