AI Materials Research Engineer

About the job

This role is 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. 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 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.
• Upto 2 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.

Qualifications

Education:

Bachelor's Degree

Skills

Certifications:

Languages:

Years of Experience:

1 - 2 Years

Work Experience:

Additional Information

Shift:

10-Day 8-Hr (United States of America)

Travel:

Yes, 10% of the Time

Relocation Eligible:

Yes

Referral Payment Plan:

Employee Referral (Standard)

Salary Range:

$131,000.00 - $180,000.00

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 committed to diversity in the workplace. All 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.