Materials Science AI Training Pod Lead

You will lead a technical review pod focused on computational materials science evaluation. The work uses scientific task specifications, simulation inputs, and reference solutions to assess whether AI-generated technical work is accurate and reliable.

You will review model outputs against project guidelines, document evidence-based judgments, and write clear feedback that can improve model behavior. You will also help maintain consistent quality across repeated review tasks.

• Evaluate AI model outputs using project rubrics and scientific evidence.
• Review source materials, simulation inputs, reference solutions, and model outputs.
• Assess scientific assumptions, numerical accuracy, simulation convergence, and computational reliability.
• Identify technical errors, edge cases, and flawed scientific assumptions.
• Explain judgments clearly and document quality standards.
• Maintain consistency across repeated technical review tasks.

What It Pays And Takes

Compensation details are provided through the project budget fields shown for this role. The project requires at least 20 hours per week and includes four hours of overlap with Pacific Time.

This role requires advanced materials science knowledge, strong programming ability, and experience reviewing complex technical or research work. Prior AI training experience is not required.

• Pay: Compensation is listed in the project budget fields.
• Time: At least 20 hours per week.
• Schedule: Four hours of overlap with Pacific Time.
• Location: Open worldwide.
• Education or experience: Ph.D., postdoctoral experience, or equivalent advanced technical experience in materials science, metallurgy, or a closely related field.
• Programming: Strong skills in Python, C/C++, Fortran, or Julia, plus proficiency with Linux and terminal-based environments.
• Technical focus: Expertise in atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
• Knowledge: Strong understanding of materials science fundamentals, numerical methods, computational modeling, and scientific validation.
• Review experience: Experience reviewing scientific code, computational simulations, manuscripts, research outputs, or other complex technical work.
• Leadership: Experience mentoring, reviewing, or leading small technical or research teams.
• Communication: Excellent written communication for precise technical feedback and quality documentation.
• Helpful tools: Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D.
• Helpful experience: Machine-learned interatomic potentials, high-throughput materials simulations, or materials databases such as Materials Project.
• Helpful systems knowledge: Docker, Git, CI/CD pipelines, automated testing, or high-performance computing environments.

How It Works

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

About AI Training Work

AI training is the human work behind many AI systems, including reviewing model answers and checking technical reasoning. Subject experts are needed because careful judgment helps models produce more accurate and reliable results.