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

Description

The company is a global leader in materials science and engineering. Its equipment is used to make most new semiconductor chips and advanced displays, and it supports AI and the commercialization of next-generation chips.

The team builds and applies physics-based simulation tools and digital twins for semiconductor process chambers and plasma processing, and makes these tools easier for engineers across the organization to use through AI-driven software and workflows.

This is a full-time role for an early-career PhD-level scientist or engineer who likes combining first-principles modeling, large-scale simulation and software development. The work sits at the crossover of computational physics, high-performance computing and AI/ML.

Responsibilities

• Apply computational modeling to real semiconductor process and equipment problems, such as thermal, plasma, deposition, etch and related multiphysics work.
• Build and improve physics-based simulation capabilities, numerical methods and engineering workflows.
• Analyze simulation, experimental and engineering data to find the physical mechanisms behind process and hardware behavior, and use the results to support design decisions.
• Develop and assess reduced-order, surrogate and machine-learning models where they clearly improve computational efficiency or prediction.
• Create AI-enabled tools and agentic workflows that help engineers set up, run, analyze and interpret computational models.
• Work with process, hardware, experimental and software teams to define modeling problems, interpret results and turn them into engineering decisions.
• Verify and validate modeling and AI-enabled capabilities against reference solutions, simulation benchmarks and experimental data.
• Help develop and maintain internal modeling software, reusable workflows, documentation and best practices.
• Explain modeling results, assumptions, limitations and recommendations clearly to scientists, engineers, technical leaders and customers.
• Build multi-scale, multi-fidelity models for thermal and plasma-based processes that link high-fidelity solvers with reduced-order and surrogate methods, with quantified accuracy and stated validity limits.
• Set up rigorous methods to verify computational and AI-enabled workflows and validate predictions against experimental data, with the infrastructure to keep them reliable as they change.

Requirements

• Hold an MS or PhD in mechanical, electrical or aerospace engineering, chemical engineering, materials science, physics or a closely related field.
• Have demonstrated experience in computational physics or multiphysics modeling in at least one area such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics or feature-scale process modeling.
• Have a strong grounding in applied mathematics and computational methods, such as numerical methods, statistical modeling, uncertainty quantification or solver development.
• Have strong programming skills in Python and/or C++, with experience building scientific or engineering applications.
• Have experience applying machine learning to scientific or engineering problems, such as surrogate modeling, physics-informed AI, neural operators or other data-driven approaches.
• Have worked with simulation data and computational models to analyze complex physical or engineering systems.
• Show strong technical judgment and attention to detail.
• Communicate well in writing and speaking.
• Preferred: hold a PhD.
• Preferred: have experience with high-performance computing, parallel computing or GPU acceleration for scientific and engineering work.
• Preferred: have experience with LLM-based agents, tool use or agentic workflow orchestration for technical or scientific applications.
• Preferred: have experience with multi-fidelity, reduced-order, surrogate or other methods that connect models across fidelity levels.
• Preferred: be familiar with commercial, open-source or in-house multiphysics platforms such as ACE+, COMSOL, ANSYS Fluent, OpenFOAM or similar tools.
• Preferred: have experience developing scientific software, reusable modeling frameworks, workflow automation or digital twins.
• Preferred: have applied computational modeling to semiconductor process or equipment work, such as thermal or plasma processing, deposition, etching or related multiphysics problems.

Conditions

Pay range: $138,000.00 - $190,000.00.

Location: Santa Clara, CA.

Full-time, New College Grad position.

Travel: 10% of the time.

Relocation assistance is available.

Pay depends on location, hire grade, job-related knowledge, skills and experience, and on internal equity with current team members.

For sales roles, the posted range is Target Total Cash, the base salary plus target bonus at 100% goal achievement.

Applications are reviewed on a rolling basis. Apply by November 20, 2026. The role may close earlier depending on application volume or candidate selection.

Benefits

• A comprehensive benefits package.
• Possible participation in a bonus program and a stock award program, where applicable.
• Programs and support for personal and professional growth and for health and wellbeing at work and at home.