Modeling Engineer 4

What You’ll Do

• Developing physics-based models for Thermal/CFD/Chemistry applications for components in semiconductor capital equipment industry. Experience in commercial software like ANSYS Fluent, Star CCM+, or 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.
• 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 advance statistical, probabilistic, and forecasting methods to best quantify design and performance of overall system. 
• Work directly with mechanical, electrical, process and software engineers to define design requirements, goals and objectives of design, CIP, testing and simulation plans.  

Who We’re Looking For

• PhD in Mechanical Engineering or closely related field with strong emphasis in Computational Fluid Dynamics, Heat transfer, 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.

Preferred Qualifications

• Preferred knowledge of chemistry, semiconductor deposition sciences, PECVD, ALD, CVD, and employing sensors and hardware designs in a vacuum environment is also a plus.
• Established skills in building ML/DL/AI models using simulation data.
• Experience with machine learning algorithms and tools (e.g., TensorFlow, PyTorch, Scikit Learn etc.) and deep learning.
• General understanding of uncertainty quantification, Bayesian optimization and probabilistic machine learning.

Our Commitment

We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.

Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.

Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories – On-site Flex and Virtual Flex. ‘On-site Flex’ you’ll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. ‘Virtual Flex’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.