Atomistic & Surface Modeling Experts (Computational Materials & Catalysis)

$84/hrrate as listed

Mercor is staffing computational materials scientists to build and grade the atomistic-simulation data a frontier lab uses to train physical-science models. Work is remote, US-based, 10–40 hrs/week, with pay observed at $84/hr.

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

What the work involves

Day to day, you alternate between authoring and judging. Authoring means constructing expert-level problems from your own research territory — a slab model with a specific termination and coverage, an NEB path whose barrier depends on the functional and the k-point sampling, a microkinetic model where the rate-determining step shifts with temperature — and writing out the reasoning a competent computational scientist would follow. Judging means reading AI-generated derivations, setups, and analyses and deciding where they are wrong, why, and how badly: an unconverged vacuum spacing, a dipole correction quietly omitted, a plausible-sounding adsorption energy referenced against the wrong gas-phase species. Most tasks end in a written rationale and a comparative rating, so the argument matters as much as the verdict.

What the platform screens for

Mercor's screen is AI-led and follow-up heavy. Expect it to push past tool names into method judgment — why you picked a particular functional or dispersion correction, how you convinced yourself a transition state was real, what you do when a surface reconstruction refuses to converge. Vague claims of "experience with DFT" collapse quickly; specific systems, codes, and failure modes hold up. Reviewers also weigh writing: whether you can compress a technical argument into a few clear sentences without hedging into uselessness. A PhD in materials science, chemistry, physics, or chemical engineering plus post-doctoral or industry research depth is effectively the floor, and semiconductor-relevant or heterogeneous-catalysis backgrounds are called out explicitly.

Logistics

Remote, US-based; long-term and ongoing rather than a one-off batch.
10 hours/week minimum, up to 40; scheduling is largely async against task deadlines.
$84/hr is the observed band for this listing, not a guaranteed rate — Mercor adjusts by specialization, screen outcome, and project.
Volume fluctuates with the client lab's data needs; treat it as a substantial side engagement unless you're taking near-full hours.

What they look for

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Have you performed first-principles calculations (DFT, ab initio MD) or classical molecular simulations (MD, Monte Carlo) as the primary responsibility in a research role or project?
Hands-on atomistic modeling · required
Have you modeled surface phenomena—including surfaces, interfaces, adsorption, or reaction mechanisms—as part of your research or professional work?
Surface chemistry or catalysis focus · required
Do you hold a PhD in materials science, physics, chemistry, chemical engineering, or a closely related discipline?
PhD in relevant field · required
Have you used at least one of the following: VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, or equivalent first-principles or molecular simulation software in a research setting?
Proficiency with standard simulation codes · required
Have you worked in computational materials research or a related field for at least two years after completing your PhD (including postdoctoral, industry, or continuing academic roles)?
Research experience beyond degree · preferred
Are you based in or able to work remotely from the United States?
US-based work eligibility · required

0 of 6 answered

Included with this listing

Interview prep — 12 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: Can you execute and interpret DFT or ab initio MD, not just name it? Do you understand what the outputs mean and how to judge whether they're credible?
A strong answer: Describe a specific system (e.g., a surface, adsorbate complex, or reaction path), name the code and key settings (functional, basis, k-points, convergence criteria), and explain what the results told you about physics or chemistry—e.g., why a site was preferred, what a barrier height meant, how you validated it.
Where people lose it: Vague language like 'I ran DFT calculations' without specifics on system, method, or interpretation. Or reciting textbook definitions instead of demonstrating hands-on confidence in troubleshooting a real calculation.