Physicist/Scientist Machine Learning

Applied Materials•Santa Clara, CA

2d•$138,000 - $190,000

About The Position

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits. We are seeking a highly motivated MS or PhD‑level scientist or engineer to develop and apply machine learning–based models using data generated from multi‑dimensional, high‑performance computing (HPC) simulations. The successful candidate will work at the intersection of physics‑based modeling, large‑scale simulation, and modern AI/ML methods to accelerate product developing in the fast-paced semiconductor equipment industry. Focus will be on developing ML models based on plasma and electromagnetic simulations. This role is ideal for candidates with strong domain knowledge in engineering or physical sciences and hands‑on experience translating complex simulation data into robust, predictive machine learning models.

Requirements

MS or PhD in Engineering (e.g., Chemical, Electrical, Mechanical, Aerospace, Nuclear, Materials), Science (e.g., Physics, Chemistry), or Computer Science
Significant experience developing machine learning or deep learning models using data from multi‑dimensional numerical simulations (e.g., PDE‑based solvers, particle‑based simulations, multiphysics models)
Strong background in Python‑based scientific computing and ML workflows
Demonstrated experience with PyTorch or equivalent deep learning frameworks
Solid understanding of: Data preprocessing and feature engineering for large, high‑dimensional datasets Model training, validation, and performance evaluation Numerical methods and/or physics‑based modeling concepts

Nice To Haves

Experience with NVIDIA Physics NeMo, NVIDIA Modulus, or related physics‑informed or simulation‑driven ML libraries
Familiarity with GPU‑accelerated computing, CUDA‑aware workflows, and HPC environments
Exposure to physics‑informed machine learning (PIML), surrogate modeling, reduced‑order modeling, or operator learning
Publications or demonstrated research contributions in ML for physical systems or related fields

Responsibilities

Develop and train machine learning and deep learning models using data from large‑scale, multi‑dimensional HPC simulations
Collaborate with domain experts to incorporate physical constraints, scientific insight, and prior knowledge into ML model design
Design workflows for data ingestion, curation, and analysis of high‑volume simulation outputs
Evaluate model accuracy, generalization, and robustness across a wide range of operating conditions
Optimize models for performance, scalability, and deployment on GPU‑accelerated platforms
Contribute to internal software tools, modeling frameworks, and best practices

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