Structural​/Thermal Modeling Engineer

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Requisition

191034

Date posted

09/24/2025

Work Location Model

On-site

Work Location

Tualatin

Work Country

United States

In the Global Products Group, we are dedicated to excellence in the design and engineering of Lam s etch and deposition products. We drive innovation to ensure our cutting-edge solutions are helping to solve the biggest challenges in the semiconductor industry.

We move atoms that move the world.

At Lam Research, we create equipment that allows chip makers to build device features more than 1,000 times smaller than a grain of sand. This tiny scale has a huge impact. Virtually every leading-edge chip inside the electronic products you use every day (TVs, smartphones, laptops, cars—even medical devices) has been made using our equipment.

As one of the world’s most trusted suppliers in the semiconductor equipment industry, we’re transforming technology. Our equipment places atoms so precisely that nearly every chip today is made using our innovations.

To build a prosperous career, start with an atom.

Should you choose to walk the path historically driven by Moore’s law, your primary job function will involve cutting edge R&D activities in the Semiconductor Equipment Industry. If solving challenges no one has faced before or attempted are of interest to you, then Lam Research is the company for you.

• Developing physics-based models for Thermo-Elastic, Structural and CFD applications for components in semiconductor capital equipment industry. Experience in commercial software like ABAQUS, ANSYS, NASTRAN, COMSOL, etc., is highly desirable.
• Data driven modeling - Juncture of physics-based modeling with data-driven, AI/ML methods:
• Developing methods for translating tool operational data into improved engineering designs
• Utilizing DOE, Optimization, and statistical methods to correlate Simulation data to experimental data.
• Stochastic optimization tools to bridge between simulation and experiments.
• Predict, measure and analyze the experimental data of mechanical designs, process specification and software programs for semiconductor manufacturing equipment.
• Uncertainty Quantification & propagation, sensitivity analysis, statistical inference for model calibration, decision making under uncertainty.

• Developing and maintaining live multi-physics models
• Multi-scale modeling from nano, meso to macro levels
• Provide design improvements of in-service tools based upon quantitative field measured failure data and less quantitative quality metrics as measured by Lam Research.
• Use advanced statistical, probabilistic and forecasting methods to best quantify design and performance of overall system.
• Provide written reports and oral presentation of results to design teams and management.
• Work directly with mechanical, electrical, process and software engineers to define design requirements, goals and objectives of design, CIP, testing and simulation plans.

Minimum Qualifications:

• PhD in Mechanical Engineering or closely related field with strong emphasis in solid mechanics, heat transfer and fluid mechanics or related fields.
• Strong ability and understanding of AI/ML concepts and hybrid physics-based AI/ML modeling software.
• Coding ability to supplement commercial software for specific applications as needs arise.
• Ability to work within a team to own and design concepts and drive design decisions.
• Ability to lead and conduct design reviews in cross functional and matrixed environments.
• Strong written and oral communication. Self-starter to start own initiatives and projects for continuous improvement in capabilities and design.
• This is a graduate eligible role.

• Preferred knowledge of chemistry, semiconductor metrology methods, and employing sensors and hardware designs in a vacuum environment is also a plus.
• Expertise or working knowledge in several of these areas are desired:
  • Big data, AI/ML algorithms and concepts.
  • Multi-physics modeling,
  • Multi-scale modeling, Feature scale from nano, meso scale to macro scale.
  • Heat transfer in low pressure regimes
  • HPC, optimization and cloud computing
  • Numer…