Pod Lead, Materials Science (AI Training) at Turing

Materials Science Pod Lead (Contractor) | Pay not stated | Remote

Turing, an AI company that works with leading AI labs, is hiring a Materials Science expert to lead a small pod of technical trainers creating terminal-based scientific tasks for a frontier AI training and evaluation initiative.

What the work involves

• Lead and mentor a pod of roughly 5-10 trainers, ensuring quality, productivity and on-time delivery of materials science tasks.
• Review tasks end to end: 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 research workflows and require multi-step scientific reasoning rather than simple database lookups.
• Verify scientific correctness across units, crystal symmetry, convergence criteria, electronic structure calculations, thermodynamic consistency and simulation parameters.
• Validate computational environments, scientific software, pseudopotentials and dependencies for reproducibility.
• Review automated graders for meaningful outputs such as lattice parameters, formation energies, band gaps, elastic constants, phonon properties, phase fractions and diffusion coefficients.
• Give clear technical feedback, track revisions and resolve quality issues before task approval.
• Onboard trainers, allocate work, monitor throughput and maintain documentation.

Who it's 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.
• Experience reviewing complex technical work, scientific code or computational simulations.
• Experience mentoring, reviewing or leading small technical or research teams.
• Excellent written communication skills for technical feedback and documentation.

Nice to have

• Experience with tools such as Quantum ESPRESSO, VASP, LAMMPS, pymatgen, ASE, phonopy, pycalphad or similar.
• Familiarity with Docker, Git, CI/CD, automated testing and HPC environments.
• Experience with scientific benchmarks, automated grading systems, or evaluating AI coding agents or LLM-generated scientific solutions.
• Industry experience in semiconductors, batteries, alloys, advanced materials or manufacturing.

Pay and schedule

• Contractor assignment (no medical/paid leave).
• 4-week contract, expected start date next week.
• At least 4 hours per day and a minimum of 40 hours per week, with 4 hours of overlap with PST.