Physics-Driven AI Engineer for Semiconductor Processes

Applied Materials, Inc.; Santa Clara (CA); On-site; USD 138,000 - 190,000; Full time

Job summary

Applied Materials, Inc. in Santa Clara, CA invites applications for a Computational Physics, Plasma Modeling & Agentic AI Engineer.

You will join a team developing physics-based simulation tools and digital twins for semiconductor process chambers and plasma processing, applying first-principles modeling and AI-driven workflows. Ideal candidates have an MS/PhD in a relevant field, strong programming in Python/C++, and experience with multiphysics modeling, ML, and HPC.

Qualifications

• MS or PhD in Mechanical, Electrical, Aerospace, Chemical Engineering, Materials Science, Physics, or related field.
• Experience in computational physics or multiphysics modeling.
• Strong programming in Python and/or C++ for scientific applications.
• Experience with machine learning for scientific applications such as surrogate modeling or physics-informed AI.
• Experience processing and analyzing simulation data.

Responsibilities

• Apply computational modeling to semiconductor process and equipment challenges (thermal, plasma, deposition, etch).
• Develop and improve physics-based simulation capabilities and workflows.
• Use data to analyze process behavior and support design decisions.
• Develop reduced-order, surrogate, and ML models for efficiency and predictive capability.
• Build AI-enabled tools and agentic workflows for engineers.

Skills

Python

C++

Machine Learning

Multiphysics modeling

Numerical methods

Uncertainty quantification

High-performance computing

Communication

Education

MS/PhD degree

Tools

ACE+

OpenFOAM

COMSOL

ANSYS Fluent

Job description

Applied Materials, Inc. in Santa Clara, CA invites applications for a Computational Physics, Plasma Modeling & Agentic AI Engineer.

You will join a team developing physics-based simulation tools and digital twins for semiconductor process chambers and plasma processing, applying first-principles modeling and AI-driven workflows. Ideal candidates have an MS/PhD in a relevant field, strong programming in Python/C++, and experience with multiphysics modeling, ML, and HPC.