The work
You will lead a pod supporting AI training for computational materials science evaluation. The work uses scientific task specifications, simulation inputs, and reference solutions to assess whether AI systems produce reliable technical results.
You will apply project rubrics, document evidence-based judgments, and explain technical issues clearly so model feedback can improve future behavior. You will also help maintain consistent quality across repeated review tasks.
- Evaluate model outputs against project guidelines and scientific reference solutions.
- Review source materials, simulation inputs, scientific code, and computational results.
- Assess scientific assumptions, numerical accuracy, simulation convergence, and result reliability.
- Identify technical errors, edge cases, and flawed scientific assumptions.
- Provide precise written feedback and document quality standards.
- Mentor, review, or lead a small technical or research team.
What it pays and takes
This is a contractor, part-time role for an experienced materials science specialist. Compensation is handled through the project budget fields shown with the role.
- Schedule: At least 4 hours per day and a minimum of 40 hours per week.
- Time-zone overlap: 4 hours of overlap with Pacific Standard Time.
- Location: Open worldwide.
- Language: Fluent 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, plus proficiency with Linux and terminal-based environments.
- Technical focus: Hands-on expertise in atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
- Scientific judgment: Strong knowledge of materials science fundamentals, numerical methods, computational modeling, and scientific validation.
- Review experience: Experience reviewing technical work, scientific code, computational simulations, manuscripts, or research outputs.
- Communication: Excellent written communication for precise technical feedback and quality documentation.
Helpful technical background
Experience with materials science software, machine-learned interatomic potentials, high-throughput simulations, materials databases, or research computing environments can support this work.
- Tools may include Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D.
- Helpful experience includes the Materials Project, Docker, Git, CI/CD pipelines, automated testing, and high-performance computing environments.
How it works
Apply on OpenTrain with your resume and then complete the application on the hiring site.
About AI training work
AI training is the human work behind systems that learn from examples, reviews, and carefully written feedback. Specialists are paid for applying subject knowledge to judge technical quality and help AI produce more reliable results.