About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Atomistic Materials Modeling Expert
Type: Contract
Compensation: $84/hour
Location: Remote
Commitment: 10–40 hours/week

Role Responsibilities

- Evaluate advanced scientific problems involving atomistic simulations and computational materials science.
- Review and validate AI-generated answers involving DFT calculations, molecular dynamics, and materials property predictions.
- Assess the appropriateness of computational methods, simulation parameters, convergence criteria, and physical assumptions.
- Analyze atomic structures, electronic properties, reaction energetics, diffusion mechanisms, and materials stability.
- Develop high-quality reference solutions, evaluation rubrics, and technically rigorous scientific explanations.
- Apply computational expertise to improve AI performance on complex materials science problems.

Qualifications

Must-Have

- PhD in Computational Materials Science, Materials Science and Engineering, Computational Chemistry, Condensed Matter Physics, Chemical Engineering, or a closely related field. Exceptional MSc candidates with substantial relevant experience may be considered.
- Demonstrated hands-on experience with at least one of the following:
- Density Functional Theory (DFT)
- Ab initio Molecular Dynamics (AIMD)
- Classical Molecular Dynamics (MD)




- Monte Carlo simulations
- Atomistic or first-principles materials modelling
- Proficiency with one or more computational tools, such as VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, or similar software.
- Strong understanding of electronic structure, interatomic interactions, thermodynamics, statistical mechanics, and materials properties.
- Experience interpreting simulation results and validating computational predictions.
- Solid Python skills for scientific computing, data analysis, or workflow automation.

Preferred

- Experience with ASE, pymatgen, or computational materials workflow tools.
- Expertise in surface and interface modelling, heterogeneous catalysis, semiconductor materials, or reaction energetics.
- Experience with high-throughput computational materials screening.
- Familiarity with machine-learned interatomic potentials and AI-assisted materials discovery.
- Peer-reviewed publications demonstrating computational materials research.
- Experience integrating experimental materials characterisation with atomistic simulations.

Application Process (Takes 20–30 mins to complete)

- Upload resume
- AI interview based on your resume
- Submit form

Resources & Support

- For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
- For any help or support, reach out to:

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

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