Remote Pod Lead - Material Science

Turing, a rapidly growing leader in AI innovation, is seeking an experienced Materials Science expert to lead a team of technical trainers focused on developing realistic, terminal-based scientific tasks for a cutting-edge AI training and evaluation initiative. This role involves overseeing a pod of 5–10 trainers, ensuring the scientific accuracy and reproducibility of materials science tasks, and maintaining high standards that reflect authentic computational materials research workflows. The successful candidate will mentor trainers, review complex tasks, and drive consistent delivery of challenging scientific problems that advance frontier AI capabilities.

Key Responsibilities:
- Lead and mentor a team of technical trainers, ensuring consistent quality, productivity, and timely delivery of materials science tasks.
- Review scientific tasks comprehensively, including problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated tests.
- Validate tasks related to density functional theory (DFT), molecular dynamics, computational thermodynamics, phase diagrams, crystallography, materials characterization, mechanical properties, semiconductors, and energy materials.
- Ensure tasks require meaningful multi-step scientific reasoning and reflect realistic materials research workflows, avoiding 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 guarantee reproducible results.
- Review automated graders to confirm they assess 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 potential shortcuts that bypass scientific calculations or simulations.
- Provide clear, actionable technical feedback, track revisions, and ensure all quality issues are resolved prior to task approval.
- Onboard new trainers, allocate work efficiently, monitor quality and throughput, escalate blockers, and maintain technical documentation and best practices.

Required Qualifications:
- Ph.D., postdoctoral experience, or equivalent advanced technical expertise in Materials Science, Metallurgy, or a closely related field.
- 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 such as atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
- Deep understanding of materials science fundamentals, numerical methods, computational modeling, and scientific validation.
- Experience reviewing complex technical work, including scientific code, computational simulations, manuscripts, or research outputs.
- Ability to critically evaluate scientific assumptions, numerical accuracy, simulation convergence, and the reliability of computational results.
- Strong analytical judgment 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 delivering precise technical feedback and documenting quality standards.

Preferred Qualifications and Benefits:
- Experience with computational materials science tools such as Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D.
- Familiarity with machine-learned interatomic potentials, high-throughput materials simulations, or materials databases like Materials Project.
- Knowledge of 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.
- Background in 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 involvement in AI training, scientific quality control, benchmark development, or evaluation programs.

Offer Details:
This is a contractor assignment requiring a commitment of at least 4 hours per day and a minimum of 40 hours per week, with a 4-hour overlap with Pacific Standard Time (PST). The contract duration is 4 weeks, with an expected start date as early as next week. Note that this role does not include medical benefits or paid leave.