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.
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.