Atomistic Materials Modeling Expert
Listing checked October 11, 2026 · pay as published by Mercor
Overview
Mercor seeks atomistic materials modeling specialists to support advanced AI research. You will judge complex problems that involve density functional theory, molecular dynamics, and materials property prediction. The work centers on scientific reasoning, computational methods, and validation of AI-generated answers. Experts also create reference solutions and rubrics that raise model performance in materials science.
What You'll Do8
- 1Evaluate advanced scientific problems in atomistic simulation and computational materials science.
- 2Review AI-generated answers that cover DFT calculations, molecular dynamics, and materials property predictions.
- 3Judge whether computational methods, simulation parameters, convergence criteria, and physical assumptions fit the problem.
- 4Examine atomic structures, electronic properties, reaction energetics, diffusion mechanisms, and materials stability.
- 5Assess surface and interface behavior, adsorption energies, defect formation, and chemical reaction pathways.
- 6Build reference solutions, evaluation rubrics, and scientific explanations that meet high standards.
- 7Spot computational errors, methodological limits, and flaws in scientific reasoning.
- 8Use computational expertise to improve AI performance on demanding materials science tasks.
Requirements12
- 1PhD in Computational Materials Science, Materials Science and Engineering, Computational Chemistry, Condensed Matter Physics, Chemical Engineering, or a related field. Outstanding MSc candidates with deep relevant experience may qualify.
- 2Hands-on work with at least one method: density functional theory (DFT), ab initio molecular dynamics (AIMD), classical molecular dynamics (MD), Monte Carlo simulations, or atomistic and first-principles materials modeling.
- 3Skill with one or more tools such as VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, or similar software.
- 4Solid grasp of electronic structure, interatomic interactions, thermodynamics, statistical mechanics, and materials properties.
- 5Experience reading simulation output and checking computational predictions.
- 6Strong Python skills for scientific computing, data analysis, or workflow automation.
- 7Experience with ASE, pymatgen, or computational materials workflow tools.
- 8Knowledge of surface and interface modeling, heterogeneous catalysis, semiconductor materials, or reaction energetics.
- 9Work with high-throughput computational materials screening.
- 10Familiarity with machine-learned interatomic potentials and AI-assisted materials discovery.
- 11Peer-reviewed publications in computational materials research.
- 12Background linking experimental materials characterization with atomistic simulations.
Who Should Apply
An ideal candidate holds a PhD in computational materials science, condensed matter physics, computational chemistry, or a nearby field, with direct use of DFT, MD, or Monte Carlo methods and fluency in VASP, LAMMPS, Python, or similar tools. The role suits researchers who can read simulation results, question convergence choices, and write clear scientific evaluations. People with only coursework in quantum mechanics or no hands-on atomistic modeling will struggle here. Common rejection reasons include a weak publication or project record in computational materials research and vague answers about simulation parameters or validation steps.
Salary Insight
Pay runs at $84 per hour, and Mercor lists the engagement as hourly. For PhD-level computational materials work tied to AI evaluation, this rate sits at the senior contractor end rather than a standard academic stipend. The source gives no fixed weekly total, so your actual earnings depend on approved hours, which can range from 10 to 40 per week.
Location
Compensation
$84/hr
Required Skills
Application Tip
Show fit with exact proof: name the DFT, AIMD, or MD methods you used, list tools such as VASP, Quantum ESPRESSO, or LAMMPS, and quantify system sizes, run counts, or property predictions you handled. Include one example where you caught a convergence or methodological error in a simulation.
How NearSkill helps you apply
1. Check your job match
We compare your resume with the job requirements to help you understand how well your experience fits.
2. Continue to the employer’s website
We’ll direct you to the employer’s application portal, where you can complete the application.
3. Get updates by email
If NearSkill is handling follow-up for this role, we’ll email you when there’s an update on your screening.
Want to check your fit first?
See how your resume matches this role before applying.
Check my job matchSimilar open positions
Explore active roles that match your skills and interests.
Mercor
VerifiedInorganic Materials, Semiconductor & Superconductor Experts
Mercor is looking for hands-on experimental scientists and engineers with deep expertise in inorganic materials, superconductors, or semiconductors to support an AI research lab focused on materials science. You'll apply your specialized scientific knowledge to create, organize, and assess the data that trains advanced models — directly influencing how those models reason about materials and devices.
Mercor
VerifiedMaterials Science Domain Expert
You will work directly with a leading AI lab's research and program management teams, applying your materials science expertise to improve how frontier models reason about real engineering work. Your main activities are validating materials knowledge tasks, writing instruction specs and golden solutions, and designing benchmarks that track model progress. Cincinnatus LLC employs you full-time on a W-2 basis, and you will operate inside the client's own tools with client-issued equipment. The role demands a PhD-level specialist with 4+ years of research or industrial R&D experience. The position is hybrid and based in the Bay Area, so you must live locally and be available on-site several days per week.
Handshake AI
VerifiedMaterials Modeling Engineer
Handshake AI seeks materials science and engineering modeling experts for part-time contract work supporting AI research. You will build expert-level questions and scenarios from real materials modeling and research engineering workflows. Your day-to-day includes reviewing AI answers for accuracy against computational simulation practices and writing structured critiques that help models improve. Work happens on your schedule with no minimum hours, and the project runs alongside your current role. Tools such as MATLAB or Materials Studio shape the technical depth expected here.
Mercor
VerifiedInorganic Chemistry & Synthesis Expert
Mercor hires PhD-level inorganic and materials chemists to support advanced AI research. You will review scientific claims, synthesis routes, and experimental results for accuracy, then build reference answers that teach models to reason through chemistry. The work spans solid-state chemistry, materials synthesis, and characterization methods such as XRD, XPS, and electron microscopy. A strong background in crystallography, thermodynamics, and reaction kinetics matters more than generic AI experience. Hours start at 10 per week and can reach 40.
Micro1
VerifiedComputational Chemistry Expert
This remote role offers a unique opportunity for a computational chemistry expert to directly shape the next generation of AI systems. You'll apply your deep knowledge of molecular simulations, quantum chemistry, and cheminformatics to design, evaluate, and improve AI benchmarks. Your work will ensure these models learn accurately from real-world chemical data, bridging the gap between scientific research and cutting-edge artificial intelligence.
Micro1
VerifiedMaterials Science Expert for AI‑Driven Engineering
A remote contractor role for a Materials Science Expert focused on advancing AI-assisted materials engineering. You’ll apply deep domain knowledge to help train next‑gen AI systems, guiding models on materials selection, failure analysis, testing, and process development. Expect to review AI-generated technical work for accuracy and deliver clear, practical feedback to support model learning. Strong communication and precise technical reasoning are essential as you collaborate with a distributed team.