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.
- Strong 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:
- For any help or support, reach out to: [email protected]
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.