Remote Surface Modeling Scientist for AI Materials Lab

Obsidian; New York (NY); Remote; USD 140,000 - 190,000; Full time

Job summary

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.

Qualifications

• Hands-on experience with first-principles or molecular methods (DFT, MD, Monte Carlo).
• Experience modeling surfaces, interfaces, adsorption or reactions.
• Background in materials science, chemistry, physics, or chemical engineering.
• Proficiency with VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, pymatgen.
• PhD in related field with several years of research beyond PhD.
• Clear written English and the ability to explain technical reasoning.

Responsibilities

• Contribute domain expertise across atomistic and surface modeling to build high-quality training data.
• Review and evaluate AI-generated scientific reasoning, improving technical accuracy.
• Design and solve expert-level problems in atomistic and surface modeling.
• Structure technical knowledge into well-organized, model-ready data.
• Deliver reliable, high-quality work within defined timelines.
• Rate and rank model outputs against defined scientific criteria with written reasoning.

Skills

Atomistic modeling

DFT

Molecular dynamics

Surface modeling

VASP

LAMMPS

pymatgen

Education

PhD in materials science / chemistry / physics

Tools

VASP

Quantum ESPRESSO

CP2K

GPAW

ASE

pymatgen

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.