Materials Science AI Training Pod Lead

You will lead and mentor a pod of about 5 to 10 technical trainers who create realistic, terminal-based computational materials science tasks. You will help maintain quality, productivity, and delivery standards.

Review scientific task materials and computational results for accuracy, reproducibility, and meaningful multi-step reasoning. Check technical work across simulation, thermodynamics, crystallography, materials characterization, and related areas.

• Review problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated tests.
• Check units, crystal symmetry, convergence criteria, electronic structure calculations, thermodynamic consistency, simulation parameters, software dependencies, and computational environments.
• Review automated graders, give actionable feedback, track revisions, resolve quality issues, allocate work, and maintain technical documentation.
• Evaluate work involving density functional theory, molecular dynamics, computational thermodynamics, phase diagrams, crystallography, mechanical properties, semiconductors, and energy materials.

What It Pays And Takes

This is a remote contractor assignment lasting four weeks. The structured listing identifies the role as entry level and part time, but the work requires advanced technical materials science experience and leadership capability.

• Pay: No rate or fixed payment is provided in the listing.
• Time: 20 or more hours per week, with at least four hours of work per day and four hours of overlap with Pacific Time.
• Location: Remote and open worldwide.
• Education and experience: Ph.D., postdoctoral experience, or equivalent advanced technical experience in materials science, metallurgy, or a related discipline.
• Programming: Strong skills in Python, C, C++, Fortran, or Julia, plus proficiency with Linux and terminal-based environments.
• Technical background: Hands-on expertise in atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
• Review skills: Experience reviewing scientific code, computational simulations, research outputs, or other complex technical work, including scientific assumptions, numerical accuracy, simulation convergence, and computational reliability.
• Leadership: Experience mentoring or leading technical or research teams is valuable.
• Helpful tools and methods: Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, DREAM3D, machine-learned interatomic potentials, high-throughput simulations, materials databases, Docker, Git, CI/CD, automated testing, high-performance computing, scientific benchmarks, and simulation-based evaluations.

How It Works

Apply on OpenTrain with your resume and then complete the application on the hiring site.

About AI Training Work

AI training work is the human work behind systems that learn from examples, including reviewing technical tasks, rating model outputs, and checking whether computer-generated results are accurate. People with specialist knowledge are needed for complex areas such as computational materials science because they can judge scientific methods, assumptions, and results.

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