Remote Atomistic & Surface Modeling Scientist

Mercor; New York (NY); Remote; USD 120,000 - 170,000; Full time

Job summary

Mercor seeks computational scientists to advance atomistic and surface modeling for a frontier AI research lab in materials science. You will apply deep, specialized knowledge to generate, structure, and evaluate scientific data that trains models, shaping how models reason about materials, surfaces, and chemical processes.

The role is hands-on and requires clear technical communication. Remote work from the US is supported, with regular collaboration across interdisciplinary teams and strict

Qualifications

• Hands-on experience with atomistic modeling using first-principles or molecular methods.
• Experience modeling surfaces, interfaces, adsorption or reaction phenomena.
• Background in computational catalysis or materials science.
• Proficiency with standard tooling like VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS.

Responsibilities

• Contribute domain expertise across first-principles and molecular simulation 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.

Skills

Atomistic modeling

DFT

Surface modeling

Adsorption phenomena

NEB

Microkinetics

VASP

Quantum ESPRESSO

CP2K

GPAW

LAMMPS

ASE

pymatgen

Python scripting

Education

PhD in materials science or related field

Tools

VASP

Quantum ESPRESSO

CP2K

GPAW

LAMMPS

ASE

pymatgen

Job description

Mercor seeks computational scientists to advance atomistic and surface modeling for a frontier AI research lab in materials science. You will apply deep, specialized knowledge to generate, structure, and evaluate scientific data that trains models, shaping how models reason about materials, surfaces, and chemical processes.

The role is hands-on and requires clear technical communication. Remote work from the US is supported, with regular collaboration across interdisciplinary teams and strict