Atomistic Modelling Expert AI Research

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 DAngelo Larry Summers and Jack Dorsey.

Position: Atomistic Materials Modeling Expert
Type: Contract
Compensation: $84/hour
Location: Remote
Commitment: 1040 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 2030 mins to complete)

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

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