Materials Science AI Training Pod Lead (Physics)

You will lead a pod of about 5 to 10 technical trainers who create and evaluate realistic, terminal-based scientific tasks. The work focuses on computational materials research and requires close review of scientific accuracy, reproducibility, code, simulations, and automated tests.

The role combines technical quality control, scientific software validation, trainer mentorship, work allocation, revision tracking, and technical documentation. The duties and qualifications reflect a materials science role with physics and terminal-based science work, so review them carefully before applying.

• Lead and mentor technical trainers while maintaining quality, productivity, and delivery standards.
• Review problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated tests.
• Validate work involving density functional theory, molecular dynamics, computational thermodynamics, phase diagrams, crystallography, materials characterization, mechanical properties, semiconductors, and energy materials.
• Check units, crystal symmetry, convergence criteria, electronic structure calculations, thermodynamic consistency, simulation parameters, dependencies, and reproducibility.
• Review automated graders for lattice parameters, formation energies, band gaps, elastic constants, phonon properties, phase fractions, diffusion coefficients, and microstructure metrics.
• Find inaccurate assumptions, numerical tolerance problems, scientific shortcuts, and ways required calculations could be bypassed.
• Give actionable feedback, track revisions, onboard trainers, allocate work, escalate blockers, and maintain technical documentation.

This is a contractor assignment with a four-week expected duration. It requires substantial weekly availability and daily overlap with Pacific Time.

• Schedule: At least 40 hours per week, including at least four hours per day.
• Time zone: At least four hours of overlap with Pacific Time.
• Language: Professional fluency in English.
• Education and experience: A 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.
• Technical environment: Proficiency with Linux and terminal-based tools.
• Scientific expertise: Hands-on experience with atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
• Review skills: Strong knowledge of materials science fundamentals, numerical methods, computational modeling, scientific validation, simulation convergence, and computational reliability.
• Leadership: Experience mentoring, reviewing, or leading small technical or research teams.
• Helpful tools and experience: 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, HPC, scientific benchmarks, or simulation-based evaluations.
• Additional relevant background: Semiconductors, batteries, alloys, advanced materials, manufacturing, publications, open-source development, or AI evaluation programs.

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

AI training work uses human-written examples, reviews, and evaluations to help improve artificial intelligence systems. People with advanced technical knowledge are needed to judge whether model outputs, code, and scientific tasks are accurate and reliable, and OpenTrain helps candidates find and apply for this kind of work.