AI Materials Research Engineer Job Santa Clara area,California USA,Research/Development

AI Materials Research Engineer

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. 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.

Applied Materials is seeking an AI Materials Research Engineer to accelerate semiconductor materials discovery using Scientific AI, Computational Materials Science, 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.

Key Responsibilities

Develop AI/ML models for:

Materials property prediction
Materials screening and optimization
Process-performance modeling
Generative materials design

Apply computational materials methodologies 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, Tensor Flow, 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.