Materials Science Chemistry Terminal Bench Science Pod Lead

You will lead and mentor a pod of about 5 to 10 technical trainers. The team develops realistic, terminal-based computational materials science tasks, meaning work completed in command-line environments.

You will review technical work for scientific accuracy, repeatability, and useful multi-step reasoning. You will also manage revisions, assign work, resolve quality issues, and maintain technical documentation.

• Review problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated tests.
• Check density functional theory, molecular dynamics, computational thermodynamics, phase diagrams, crystallography, materials characterization, mechanical properties, semiconductors, and energy materials work.
• Check units, crystal symmetry, convergence criteria, electronic structure calculations, thermodynamic consistency, simulation settings, software dependencies, and computational environments.
• Review automated graders and give clear, useful feedback to improve technical quality.
• Track revisions, allocate work, and maintain delivery and quality standards for the trainer pod.

The role details do not specify a pay rate. This is a remote contractor assignment lasting four weeks.

• Pay: Not specified in the role details.
• Schedule: 20+ hours per week, with at least four hours of work per day.
• Time overlap: At least four hours overlapping with Pacific Time.
• Location: Remote and open worldwide.
• Education or experience: A 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.
• Environment: Proficiency with Linux and terminal-based work.
• Technical background: Hands-on expertise in atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
• Review experience: Experience assessing scientific code, computational simulations, research outputs, scientific assumptions, numerical accuracy, convergence, and computational reliability.
• Helpful experience: Mentoring or leading technical or research teams.
• Helpful tools: Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D.
• Additional helpful background: Machine-learned interatomic potentials, high-throughput simulations, materials databases, Docker, Git, CI/CD, automated testing, high-performance computing, scientific benchmarks, or simulation-based evaluations.
• Language: Professional fluency in English.

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

The listed title and role details use different subject labels, so review the duties and qualifications before applying.

AI training work is the human work behind artificial intelligence, including creating, reviewing, and rating examples that help models perform better. Experienced specialists are needed for complex subjects because they can judge whether technical answers, code, and scientific reasoning are accurate and reliable.