Principal AI Data Scientist – Scientific AI & Physics-Informed Machine Learning

Applied Materials
Santa Clara
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: phdSkills: ["Communication","Collaboration","Technical leadership"]

Develop and deploy advanced AI/ML models for semiconductor process and device simulations, EDA, packaging, reliability, and manufacturing workflows. Build physics-informed and hybrid models that combine experimental data, simulation outputs, and domain knowledge, including surrogate modeling and scientific ML frameworks. Research state-of-the-art deep learning and foundation-model techniques (e.g., GNNs, neural operators, generative AI) and collaborate with semiconductor experts to transition research into production solutions while driving technical thought leadership.

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Applied Materials
Applied Materials
2 days ago

Principal AI Data Scientist – Scientific AI & Physics-Informed Machine Learning

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Last checked: 3 hours agoStatus: Live

Job Summary

Develop and deploy advanced AI/ML models for semiconductor process and device simulations, EDA, packaging, reliability, and manufacturing workflows. Build physics-informed and hybrid models that combine experimental data, simulation outputs, and domain knowledge, including surrogate modeling and scientific ML frameworks. Research state-of-the-art deep learning and foundation-model techniques (e.g., GNNs, neural operators, generative AI) and collaborate with semiconductor experts to transition research into production solutions while driving technical thought leadership.
Location: Santa Clara
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Develop and deploy AI/ML solutions for semiconductor process and device simulations, EDA, packaging, reliability, and manufacturing applications.
  • •Create physics-informed and hybrid AI models integrating experimental data, simulation outputs, and domain knowledge.
  • •Build surrogate models and scientific machine learning frameworks to accelerate computationally intensive simulation and engineering workflows.
  • •Research and apply state-of-the-art techniques including deep learning, generative AI, graph neural networks, neural operators, and foundation models.
  • •Collaborate with semiconductor experts, software engineers, and product teams to transition research into production solutions.
Travel: Low travel

Key Requirements

  • •PhD in Electrical Engineering, Physics, Materials Science, Computer Science, Applied Mathematics, Computational Science, or a related discipline.
  • •Strong expertise in machine learning, deep learning, statistical modeling, and scientific computing.
  • •Hands-on experience with Python and modern AI frameworks such as PyTorch, TensorFlow, or JAX.
  • •Strong background in numerical methods, optimization, simulation, or computational modeling.
  • •Excellent communication skills and ability to work across multidisciplinary teams.
Experience:Semiconductor industryScientific computingAI/MLGenerative AI
Education:PhD / Doctorate
Skills:CommunicationCollaborationTechnical leadership
Tech Stack:PythonPyTorchTensorFlowJAXPhysics-informed neural networksPINNsSurrogate modelingUncertainty quantificationDigital twinsTCADFEMCFDMonte CarloMultiphysics simulationGraph neural networksNeural operatorsFoundation modelsGenerative AIMultimodal learningElectronic design automation (EDA)

Company Brief

Applied Materials
Provides semiconductor manufacturing equipment, services, and software used to build advanced chips and display technologies. Its tools support materials engineering, deposition, etching, inspection, and process control for major electronics manufacturers worldwide.
Industry: Industrial Machinery
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1967
Glassdoor
Glassdoor: 3.9
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