Role brief
Job description
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)