Modeling Engineer - Plasma Etch, Machine Learning

Applied Materials • Santa Clara, CA

8d • Onsite

About The Position

We are seeking a highly motivated PhD-level scientist or engineer to develop and apply advanced modeling capabilities for plasma processing applications, including plasma etching, plasma-enhanced deposition, and atomic layer etching (ALE). This role focuses on understanding, modeling, and predicting plasma–surface interaction processes using a combination of physics-based methods and data-driven approaches. The ideal candidate will have hands-on experience with atomistic and mesoscale modeling techniques such as ab initio quantum chemistry, molecular dynamics (MD), and kinetic Monte Carlo (kMC). In addition, the candidate will be expected to develop and deploy machine learning (ML) models trained on data generated from large-scale, multi-dimensional high-performance computing (HPC) simulations, complemented by experimental measurements. This position offers a unique opportunity to work at the intersection of first-principles modeling, large-scale simulation, and modern AI/ML methods, contributing directly to accelerated technology and product development in the fast-paced semiconductor equipment industry.

Requirements

• PhD in Engineering (e.g., Chemical, Materials, Mechanical) or Science (e.g., Physics, Chemistry); exceptional MS candidates with relevant experience will be considered
• Demonstrated experience modeling plasma–surface or gas–surface interaction processes using ab initio quantum chemistry, molecular dynamics, and/or kinetic Monte Carlo methods
• Strong background in machine learning, particularly in the context of scientific computing or HPC environments
• Experience working with large, complex simulation datasets and integrating modeling results with experimental data

Responsibilities

• Develop and apply advanced models for plasma etching, deposition, and plasma-based surface modification processes
• Combine physics-based and ML-driven approaches to improve process understanding and predictive capability
• Collaborate closely with experimental teams to guide process development and interpret new experimental results
• Contribute to the development, validation, and maintenance of internal software tools, modeling frameworks, and best practices
• Communicate modeling results clearly and effectively to multidisciplinary teams, including scientists, engineers, and major customers

What This Job Offers

• Job Type: Full-time
• Education Level: Ph.D. or professional degree
• Number of Employees: 11-50 employees