Lead Remote Computational Materials Scientist (PhD)

Obsidian; New York (NY); Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Remote work (US-based)

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 atomistic modeling using first-principles or molecular methods.
• Experience modeling surfaces, interfaces and adsorption/reaction phenomena.
• Proficiency with standard tooling for atomistic simulations (VASP, QE, CP2K, LAMMPS).
• Clear written English and ability to explain technical reasoning concisely.

Responsibilities

• Contribute domain expertise across first-principles and molecular simulation to build 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 into well-organized, model-ready data.

Skills

Atomistic modeling

First-principles methods

Molecular dynamics

DFT

Materials science knowledge

Education

PhD in materials science, chemistry, physics, chemical engineering, or related field

Tools

VASP

Quantum ESPRESSO

CP2K

GPAW

LAMMPS

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.