Job description

Who We Are Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology. What We Offer Salary: $170,000.00 - $234,000.00 Location: Santa Clara,CA 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 . Role Summary Applied Materials is seeking an AI MaterialsResearch Engineer to accelerate semiconductor materials discovery using Scientific AI, Computational MaterialsScience, and Machine Learning . The role combines materials science expertise with AI/ML, simulation, and data-driven modeling to develop next-generation materials and process innovations. Based on related internal Materials AI role descriptions. Key Responsibilities Develop AI/ML models for: Materials property prediction Materials screening and optimization Process-performance modeling Generative materials design Apply computational materialsmethodologies including: Density Functional Theory (DFT) Molecular Dynamics (MD) Kinetic Monte Carlo (kMC) Phase-field and Monte Carlo simulations Build AI surrogate models to accelerate simulation-driven research. Create materials informatics pipelines integrating: Experimental data Characterization results Simulation outputs Scientific literature Develop AI copilots and agentic workflows for: Literature review Hypothesis generation Experiment planning Simulation orchestration Collaborate with materials scientists, process engineers, and AI teams to deliver Scientific AI solutions. Required Qualifications MS/PhD in Materials Science, Computational Materials Science, Physics, Chemical Engineering, or related field. 2–5 years of experience in Computational Materials Science, Materials Informatics, Scientific ML, or AI for scientific applications. Strong Python programming and ML experience (PyTorch, TensorFlow, Scikit-Learn). Experience with one or more computational methods: DFT MD kMC Phase-Field Modeling Strong understanding of: Crystal structures Thermodynamics Kinetics Defect physics Semiconductor materials Preferred Qualifications Experience with simulation platforms such as VASP, Quantum Espresso, CP2K, LAMMPS, or GROMACS. Experience with Materials Project, OQMD, NOMAD, or similar databases. Familiarity with: Graph Neural Networks (GNNs) Materials Foundation Models Physics-Informed ML Generative AI for materials design Experience using cloud/HPC environments for large-scale model training and simulations. Additional Information Time Type: Full time Employee Type: Assignee / Regular Travel: Not Specified Relocation Eligible: No 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. Applied Materials is an Equal Opportunity Employer. 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. In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at Accommodations_Program amat.com, or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.

Requirements

Must-haves

Santa Clara,CA
Key Responsibilities Develop AI/ML models for: Materials property prediction
Materials screening and optimization
Process-performance modeling
Generative materials design
Apply computational materialsmethodologies including: Density Functional Theory (DFT)
Molecular Dynamics (MD)
Kinetic Monte Carlo (kMC)
Phase-field and Monte Carlo simulations
Build AI surrogate models to accelerate simulation-driven research.
Create materials informatics pipelines integrating: Experimental data
Characterization results
Simulation outputs
Scientific literature
Develop AI copilots and agentic workflows for: Literature review
Hypothesis generation
Experiment planning
Simulation orchestration
Collaborate with materials scientists, process engineers, and AI teams to deliver Scientific AI solutions.
Required Qualifications MS/PhD in Materials Science, Computational Materials Science, Physics, Chemical Engineering, or related field.
2–5 years of experience in Computational Materials Science, Materials Informatics, Scientific ML, or AI for scientific applications.
Strong Python programming and ML experience (PyTorch, TensorFlow, Scikit-Learn).
Experience with one or more computational methods: DFT

Nice-to-haves

Preferred Qualifications Experience with simulation platforms such as VASP, Quantum Espresso, CP2K, LAMMPS, or GROMACS.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomachine learningmlpythonaccessibility

Platform: workday
Effort: long