AI Materials Research Engineer

Job Title

AI Materials Research Engineer

Role Summary

Develop and deploy Scientific AI and computational materials-science methods to accelerate semiconductor materials discovery and process innovation. The role combines materials modeling, simulation, machine learning, and data integration to produce predictive models and workflows that inform experiments and process development.

Experience Level

Mid-level. Typical background: 2–5 years in computational materials science, materials informatics, scientific machine learning, or AI applied to scientific problems.

Responsibilities

Work on AI/ML and simulation-driven projects to identify and optimize materials and process performance.

• Develop ML models for materials property prediction, screening, process-performance modeling, and generative materials design.
• Apply computational materials methods such as DFT, molecular dynamics, kinetic Monte Carlo, and phase-field simulations to generate data and validate models.
• Build surrogate models to accelerate simulation-driven research and reduce computational cost.
• Create materials-informatics pipelines that integrate experimental data, characterization results, simulation outputs, and literature.
• Develop AI copilots and agentic workflows for literature review, hypothesis generation, experiment planning, and simulation orchestration.
• Collaborate with materials scientists, process engineers, and AI teams to deliver production-ready Scientific AI solutions.

Requirements

Core technical skills and domain experience required; preferred skills listed separately.

Must-have

• 2–5 years of relevant experience in computational materials science, materials informatics, scientific ML, or related fields.
• Strong Python programming and machine-learning experience (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with one or more computational methods: DFT, molecular dynamics, kinetic Monte Carlo, or phase-field modeling.
• Demonstrated understanding of crystal structures, thermodynamics, kinetics, defect physics, and semiconductor materials.
• Experience building and validating surrogate models and integrating heterogeneous scientific data sources.

Nice-to-have

• Experience with simulation packages (VASP, Quantum Espresso, CP2K, LAMMPS, GROMACS).
• Familiarity with materials databases (Materials Project, OQMD, NOMAD) and materials informatics ecosystems.
• Experience with graph neural networks, materials foundation models, physics-informed ML, or generative AI for materials design.
• Experience using cloud or HPC environments for large-scale model training and simulations.

Education Requirements

MS or PhD in Materials Science, Computational Materials Science, Physics, Chemical Engineering, or a related technical field.

About the Company

Company: Applied Materials

Headquarters: Santa Clara, CA, United States

Global leader in materials engineering solutions for the semiconductor and display industries, designing, manufacturing, and servicing equipment used to produce chips and advanced displays worldwide. Enables customers' production of semiconductor devices and display technologies and supports innovations like AI and IoT.