Physicist/Scientist Machine Learning
Role details
Job location
Tech stack
Job description
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., * 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
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, * 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
Benefits & conditions
The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.
For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.
