Computational Materials Scientist - PhD

Mercor is seeking computational scientists specializing in atomistic and surface modeling to support a frontier AI research lab building models for materials science and the physical sciences. This is hands-on, expert-level work: you'll apply deep, specialized knowledge to generate, structure, and evaluate the scientific data these models learn from — and your input will directly shape how advanced models reason about materials, surfaces, and chemical processes.

Key Responsibilities:

• Contribute domain expertise across first-principles and molecular simulation — electronic structure, surface and interface modeling, adsorption, and reaction energetics — to build high-quality training and evaluation data.
• Review and evaluate AI-generated scientific reasoning, catching errors and improving technical accuracy.
• Design and solve challenging, expert-level problems in atomistic and surface modeling.
• Rate and rank model outputs against defined scientific criteria, with clear written reasoning.
• Structure technical knowledge — simulation setups, methods, and results — into well-organized, model-ready data.
• Deliver reliable, high-quality work within defined timelines.

You’re a strong fit if you have:

• Hands‑on experience with atomistic modeling using first-principles or molecular methods (DFT, ab initio molecular dynamics, classical MD, or Monte Carlo).
• Experience modeling surfaces, interfaces, and adsorption or reaction phenomena (slab models, surface reconstructions, transition states, NEB, microkinetics).
• Experience modeling semiconductor-relevant materials, or a background in computational (heterogeneous) catalysis.
• Proficiency with standard tooling (e.g., VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, pymatgen).
• A 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 explain technical reasoning concisely.

Role Details:

• Type: Long-term, ongoing engagement
• Engagement: Up to 40 hours/week (minimum 10)
• Work arrangement: Remote (US-based)

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