Physicist/Scientist Machine Learning

We are seeking a highly motivated MS or PhD\u2011level scientist or engineer to develop and apply machine learning\u2013based models using data generated from multi\u2011dimensional, high\u2011performance computing (HPC) simulations. The successful candidate will work at the intersection of physics\u2011based modeling, large\u2011scale 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.\n\nThis role is ideal for candidates with strong domain knowledge in engineering or physical sciences and hands\u2011on experience translating complex simulation data into robust, predictive machine learning models.\n\nRequired Qualifications\n\n * MS or PhD in Engineering (e.g., Chemical, Electrical, Mechanical, Aerospace, Nuclear, Materials), Science (e.g., Physics, Chemistry), or Computer Science\n * Significant experience developing machine learning or deep learning models using data from multi\u2011dimensional numerical simulations (e.g., PDE\u2011based solvers, particle\u2011based simulations, multiphysics models)\n * Strong background in Python\u2011based scientific computing and ML workflows\n * Demonstrated experience with PyTorch or equivalent deep learning frameworks\n * Solid understanding of:\n * Data preprocessing and feature engineering for large, high\u2011dimensional datasets\n * Model training, validation, and performance evaluation\n * Numerical methods and/or physics\u2011based modeling concepts\n\n\n\nPreferred Qualifications\n\n * Experience with NVIDIA Physics NeMo, NVIDIA Modulus, or related physics\u2011informed or simulation\u2011driven ML libraries\n * Familiarity with GPU\u2011accelerated computing, CUDA\u2011aware workflows, and HPC environments\n * Exposure to physics\u2011informed machine learning (PIML), surrogate modeling, reduced\u2011order modeling, or operator learning\n * Publications or demonstrated research contributions in ML for physical systems or related fields\n\n\n\nKey Responsibilities\n\n * Develop and train machine learning and deep learning models using data from large\u2011scale, multi\u2011dimensional HPC simulations\n * Collaborate with domain experts to incorporate physical constraints, scientific insight, and prior knowledge into ML model design\n * Design workflows for data ingestion, curation, and analysis of high\u2011volume simulation outputs\n * Evaluate model accuracy, generalization, and robustness across a wide range of operating conditions\n * Optimize models for performance, scalability, and deployment on GPU\u2011accelerated platforms\n * Contribute to internal software tools, modeling frameworks, and best practices\n\n\n\n## Qualifications\n\n### Education:\n\nMaster\u0027s Degree\n\n### Skills\n\n### Certifications:\n\n### Languages:\n\n### Years of Experience:\n\n4 - 7 Years\n\n### Work Experience:\n\n## Additional Information\n\n### \n\n### Shift:\n\n10-Day 8-Hr (United States of America)\n\n### \n\n### Travel:\n\nYes, 10% of the Time\n\n### \n\n### Relocation Eligible:\n\nYes\n\n### Referral Payment Plan:\n\nNone\n\nU.S. Salary Range:\n\n$138,000.00 - $190,000.00\n\nThe 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. \n\nFor 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.\n\nApplied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law. \n