Computational Materials Scientist for AI Model Training

Role Overview

Apply advanced atomistic and surface-modeling expertise to create, organize, and assess scientific data used to train and evaluate AI models for materials science and the physical sciences. Your work will help improve how these models reason about materials, surfaces, and chemical processes.

Key Responsibilities

• Apply expertise in first-principles and molecular simulation, including electronic structure, surface and interface modeling, adsorption, and reaction energetics, to develop high-quality training and evaluation data.
• Review AI-generated scientific reasoning, identify technical errors, and improve accuracy.
• Design and solve complex atomistic and surface-modeling problems.
• Rate and rank model outputs using defined scientific criteria, providing clear written rationale.
• Organize simulation setups, methods, and results into structured, model-ready technical data.
• Deliver dependable, high-quality work within established timelines.

Qualifications

• Hands-on atomistic-modeling experience using first-principles or molecular methods, such as DFT, ab initio molecular dynamics, classical MD, or Monte Carlo.
• Experience modeling surfaces, interfaces, adsorption, or reaction phenomena, including slab models, surface reconstructions, transition states, NEB, or microkinetics.
• Experience with semiconductor-relevant materials or a background in computational heterogeneous catalysis.
• Proficiency with tools such as VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, or pymatgen.
• PhD in materials science, chemistry, physics, chemical engineering, or a related field, ideally with several years of research experience beyond the PhD.
• Clear written English and the ability to communicate technical reasoning concisely.

Work Terms

• Long-term, ongoing hourly engagement.
• Remote work for candidates based in the United States.
• Commitment of at least 10 hours per week, with up to 40 hours per week available.

Compensation

• 84 per hour.