AI-Driven Materials Scientist: Computational Design

Applied Materials, Inc.; Santa Clara (CA); On-site; USD 170,000 - 234,000; Full time

Job summary

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

The role blends materials science with AI/ML, simulation, and data-driven modeling to develop next-generation materials and process innovations. The ideal candidate has a PhD in a related field, 2–5 years of experience, strong Python/ML skills, and hands-on experience with DFT/MD/kMC and Phase-field

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.
• > Ability to collaborate with materials scientists, process engineers, and AI teams.

Responsibilities

• > Develop AI/ML models for materials property prediction and screening.
• < Build AI surrogate models to accelerate simulation-driven research.
• < Create materials informatics pipelines from experimental data to results.
• < Collaborate with cross-functional teams to deliver Scientific AI solutions.
• < Apply DFT/MD/kMC/Phase-field methods in research workflow.

Skills

Python

ML for science

GNNs

Physics-informed ML

Generative AI

Education

MS/PhD in Materials Science or related field

Tools

VASP

Quantum Espresso

CP2K

LAMMPS

GROMACS

Materials Project

OQMD

NOMAD

Job description

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

The role blends materials science with AI/ML, simulation, and data-driven modeling to develop next-generation materials and process innovations. The ideal candidate has a PhD in a related field, 2–5 years of experience, strong Python/ML skills, and hands-on experience with DFT/MD/kMC and Phase-field