Computational Materials Scientist - PhD

Obsidian; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Mercor seeks computational scientists to support a frontier AI research lab for materials science and the physical sciences. You will apply deep, specialized knowledge to generate, structure, and evaluate data that trains models for materials, surfaces, and chemical processes.

You will review AI-generated reasoning, design expert-level atomistic problems, and organize results into model-ready datasets with timely delivery.

Qualifications

• Hands-on experience with atomistic modeling using first-principles or molecular methods (DFT, ab initio MD, 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.

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.

Skills

Atomistic modeling

First-principles methods

DFT

Molecular dynamics

Surface modeling

NEB

pymatgen

LAMMPS

Education

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

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.

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)