Computational Scientist for AI Model Evaluation in Materials Science

Bring deep atomistic and surface modeling expertise to a frontier AI research lab focused on materials science and physical sciences. This hands-on role uses specialist simulation and surface science knowledge to generate, organize, and evaluate the scientific data that advanced models learn from. Your work will directly influence how models reason about electronic structure, surfaces, adsorption, and reaction energetics.

• Provide domain expertise across first-principles and molecular simulation, including electronic structure, surface and interface modeling, adsorption, and reaction energetics, to produce high quality training and evaluation data.
• Design and solve expert-level atomistic and surface modeling problems, including setup, execution, and interpretation of simulations.
• Review and evaluate AI-generated scientific reasoning, identify errors, and improve technical accuracy.
• Rate and rank model outputs against defined scientific criteria, documenting clear written justification for assessments.
• Structure technical knowledge into well organized, model-ready data, including simulation setups, methods, and results.
• Deliver reliable, high quality work on defined timelines.

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

• Engagement type: Long-term, ongoing engagement.
• Schedule: Up to 40 hours per week, with a minimum commitment of 10 hours per week.
• Work arrangement: Remote, candidates must be based in the United States.
• Employment form: Hourly engagement.

84 per hour

• Candidates must be located in the United States and able to work remotely within US time zones.