Computational Chemist / Materials Scientist (Machine Learning – Reactive MLIPs)

• Reactive processes
• Diffusion and transport
• Oxidation/reduction
• Surface and interface evolution

Required Qualifications

• Ph.D. in Materials Science, Physics, Chemistry, or related field.
• Demonstrated expertise in reactive machine-learned interatomic potentials (MLIPs) capable of modeling bond breaking and formation.
• Hands-on experience with one or more MLIP frameworks:
  • MACE, NequIP, GAP, SNAP, DeepMD, or equivalent
• Strong background in first-principles methods (DFT) and atomistic simulations (MD).
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Experience working in HPC environments and handling large-scale simulations.
• Proven ability in dataset generation, labeling strategies, and model validation for ML-based atomistic models.

Core Technical Competencies

• Reactive MLIP development and deployment
• Machine learning for atomistic simulations
• Molecular dynamics and reaction modeling
• Materials informatics and data pipelines
• High-performance scientific computing

Analytical & Reasoning Requirements

• Strong analytical, logical reasoning, and quantitative problem-solving skills.
• Demonstrated ability to:
  • Diagnose and debug ML model failures and training instabilities
  • Critically evaluate model predictions against physical principles
  • Ensure physical consistency, transferability, and robustness of simulations
  • Identify gaps in training data and design targeted data acquisition strategies
• Ability to translate complex physical phenomena into tractable computational models.

Preferred Qualifications

• Experience in reactive systems, including:
  • Surface chemistry
  • Catalysis
  • Oxidation/reduction reactions
  • Semiconductor or interface materials
• Familiarity with uncertainty quantification, Bayesian methods, and active learning.
• Experience with:
  • ASE, LAMMPS, VASP, or similar tools
  • Workflow frameworks (FireWorks, AiiDA, etc.)
• Exposure to graph neural networks (GNNs) and equivariant architectures.
• Industry experience in materials development or process modeling.

Additional Information

Time Type:

Full time

Employee Type:

New College Grad

Travel:

Yes, 10% of the Time

Relocation Eligible:

Yes

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.

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