New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)

Applied Materials is hiring a New College Grad to help build computational physics and agentic AI engineering capabilities for semiconductor process chambers. Based in Santa Clara, CA and working onsite, you will support physics-based simulation tools and digital twins used to improve both prediction and usability across complex, physics-driven processes.

The work combines computational modeling, verification and validation, and AI-enabled workflows that support engineers through the setup, execution, analysis, and interpretation of simulation results. This full-time role is intended for a Spring 2027 start and supports relocation for eligible candidates.

Key Responsibilities

• Apply computational modeling to semiconductor process and equipment development challenges, including thermal, plasma, deposition, etch, and related multiphysics applications.
• Develop, implement, and improve physics-based simulation capabilities, numerical methods, and engineering workflows.
• Use simulation, experimental, and engineering data to analyze process and hardware behavior, identify key physical mechanisms, and support design decisions.
• Develop and evaluate reduced-order, surrogate, and machine-learning models to improve computational efficiency and/or predictive capability.
• Build AI-enabled tools and agentic workflows that help engineers set up, run, analyze, and interpret computational models.
• Collaborate with process, hardware, experimental, and software teams to define modeling problems and translate modeling insights into engineering decisions.
• Verify and validate modeling and AI-enabled capabilities using reference solutions, simulation benchmarks, and experimental data.
• Contribute to internal modeling software and the maintenance of reusable workflows, documentation, and best practices.
• Communicate modeling results, assumptions, limitations, and recommendations to scientists, engineers, technical leadership, and customers.

Required Qualifications

• MS or PhD in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science, Physics, or a related field.
• Demonstrated experience in computational physics or multiphysics modeling, including areas such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics, or feature-scale process modeling.
• Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, uncertainty quantification, or computational solver development.
• Strong programming experience in Python and/or C++, including development of scientific or engineering applications.
• Experience with machine learning for scientific or engineering applications, including surrogate modeling, physics-informed AI, neural operators, or related data-driven approaches.
• Experience analyzing complex physical or engineering systems using simulation data and computational models.
• Strong technical judgment and attention to detail.
• Excellent written and verbal communication skills.

Three Core Areas of Work

• Modeling Capability: develop and apply advanced multi-scale and multi-fidelity models for thermal and plasma-based semiconductor processes, linking high-fidelity solvers with reduced-order and surrogate approaches, with quantified accuracy and clearly defined limits of validity.
• Verification & Validation: establish rigorous methods to verify computational models and AI-enabled workflows against reference solutions, and validate predictions against experimental data, including the infrastructure needed to maintain reliability as capabilities evolve.

Technologies

• Python, C++, machine learning, surrogate modeling, physics-informed AI, neural operators
• LLM-based agents, high-performance computing, parallel computing, GPU acceleration
• ACE+, COMSOL, ANSYS Fluent, OpenFOAM

Job Details

• Location: Santa Clara, CA (onsite)
• Salary: USD 138,000 - 190,000 per year
• Time Type: Full time
• Employee Type: New College Grad
• Travel: Yes, 10% of the time
• Relocation Eligible: Yes