Pod Lead — Material Sciences (Terminal Bench Science)
TuringPosted Oct 22, 2026
Remote
About Turing
Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems.
Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
Role Overview
We are seeking an experienced Materials Science expert to lead a team of technical trainers developing realistic, terminal-based scientific tasks for a frontier AI training and evaluation initiative.
In this role, you will oversee a pod of approximately 5–10 trainers, review materials science tasks for technical accuracy, reproducibility, and evaluation quality, and ensure tasks reflect authentic computational materials research workflows. You will also mentor trainers, maintain quality standards, and drive consistent delivery of challenging scientific tasks.
What you'll do
Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of materials science tasks.
Review scientific tasks end to end, including problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated tests.
Validate tasks involving density functional theory (DFT), molecular dynamics, computational thermodynamics, phase diagrams, crystallography, materials characterization, mechanical properties, semiconductors, and energy materials.
Ensure tasks reflect realistic materials research workflows and require meaningful multi-step scientific reasoning rather than simple database lookups or library calls.
Verify scientific correctness across units, crystal symmetry, convergence criteria, electronic structure calculations, thermodynamic consistency, and simulation parameters.
Validate computational environments, scientific software, pseudopotentials, interatomic potentials, and dependencies to ensure reproducible results.
Review automated graders to ensure they evaluate meaningful scientific outputs such as lattice parameters, formation energies, band gaps, elastic constants, phonon properties, phase fractions, diffusion coefficients, and microstructure metrics.
Identify technical inaccuracies, unrealistic assumptions, incorrect numerical tolerances, and opportunities to bypass scientific calculations or simulations.
Provide clear, actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
Onboard trainers, allocate work, monitor quality and throughput, escalate blockers, and maintain technical documentation and best practices.
What we're looking for
Ph.D., postdoctoral experience, or equivalent advanced technical experience in Materials Science, Metallurgy, or a closely related discipline.
Strong programming skills in Python, C/C++, Fortran, or Julia, with proficiency in Linux and terminal-based environments.
Hands-on expertise in at least one major area: atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
Strong understanding of materials science fundamentals, numerical methods, computational modeling, and scientific validation.
Experience reviewing complex technical work, scientific code, computational simulations, manuscripts, or research outputs.
Ability to evaluate scientific assumptions, numerical accuracy, simulation convergence, and the reliability of computational results.
Strong analytical judgment with the ability to identify technical errors, edge cases, and flawed scientific assumptions.
Experience mentoring, reviewing, or leading small technical or research teams.
Excellent written communication skills for providing precise technical feedback and documenting quality standards.
Nice to have
Experience with computational materials science tools such as Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D.
Experience with machine-learned interatomic potentials, high-throughput materials simulations, or materials databases such as Materials Project.
Familiarity with Docker, Git, CI/CD pipelines, automated testing, and high-performance computing (HPC) environments.