Atomistic Materials Modeling Expert

$84/hr

rate as listed

Workload: 10+ hrs/week
Engagement: Project-based
Location: Worldwide
Field: Sciences

Evaluate AI-generated solutions to advanced atomistic materials modelling problems, including DFT, molecular dynamics, energetics and materials property predictions. The work combines technical review, error diagnosis, reference-solution writing and rubric development for scientific AI research.

The work

Day to day, experts review technically demanding materials science problems and assess AI-generated answers for physical validity, methodological fit and numerical rigor. Tasks can cover electronic structure, atomic stability, diffusion, defects, adsorption, surfaces, interfaces and reaction pathways, with close attention to simulation parameters, convergence criteria and unsupported assumptions.

What Mercor screens for

The screen is likely to test whether you can discuss specific calculations you have run, defend choices such as functionals, pseudopotentials, ensembles and boundary conditions, and identify errors in computational reasoning. Hands-on experience with DFT, AIMD, classical MD, Monte Carlo or related first-principles modelling is required, along with proficiency in VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS or similar software. Python is required for scientific computing, analysis or workflow automation. ASE, pymatgen, high-throughput workflows, machine-learned interatomic potentials and links to experimental characterisation are preferred experience rather than stated requirements.

Logistics

The listed commitment is a minimum of 10 hours per week, with potential availability of up to 40 hours per week. The listing does not specify a typical schedule, time-zone requirements, whether work is asynchronous or whether the engagement is formally remote. Pay band: $84/hr

What they look for

PhD in a relevant computational materials field, or an MSc with substantial relevant experience
Hands-on DFT, AIMD, classical MD, Monte Carlo or atomistic modelling experience
Proficiency with VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS or similar software
Electronic structure, thermodynamics, statistical mechanics and interatomic interaction fundamentals
Simulation validation, convergence testing and interpretation of materials predictions
Python for scientific computing, data analysis or workflow automation

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether your claimed experience is tied to a verifiable calculation and whether you can separate your own contribution from that of collaborators.
A strong answer: Use one concrete project and state the material, property or mechanism studied, the code and method used, your direct responsibilities and how the prediction was validated. Include a publication, thesis chapter, repository or other checkable output if available.
Where people lose it: Candidates lose points by summarising a research area without identifying a calculation they personally set up, ran or interpreted.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.