Remote Pod Lead - Material Science

Turing

1d•Remote

About The Position

Turing is seeking an experienced Materials Science expert to lead a pod of approximately 5–10 technical trainers creating realistic, terminal-based scientific tasks for a frontier AI training and evaluation initiative. The Pod Lead oversees task quality, technical accuracy, reproducibility, and timely delivery, while mentoring trainers and maintaining consistent standards. Tasks should reflect authentic computational materials research workflows and require meaningful, multi-step scientific reasoning. The role includes reviewing tasks end to end, including problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated tests. The Pod Lead validates scientific and computational content across areas such as density functional theory, molecular dynamics, computational thermodynamics, phase diagrams, crystallography, materials characterization, mechanical properties, semiconductors, and energy materials. They also verify computational environments and software dependencies to support reproducible results, and assess automated graders for meaningful scientific outputs. The assignment requires at least 4 hours per day and a minimum of 40 hours per week, with 4 hours of overlap with PST. It is a contractor assignment with no medical or paid leave, for a four-week duration. The expected start date is next week.

Requirements

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.
Commit to at least 4 hours per day and a minimum of 40 hours per week, with 4 hours of overlap with PST.

Nice To Haves

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.
Experience developing scientific benchmarks, programming challenges, simulation-based evaluations, or automated grading systems.
Experience developing or evaluating AI coding agents, terminal-based agents, or LLM-generated scientific solutions.
Industry experience in semiconductors, batteries, alloys, advanced materials, or manufacturing.
Publications, research contributions, or open-source development experience in computational materials science.
Prior experience in AI training, scientific quality control, benchmark development, or evaluation programs.

Responsibilities

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.