Remote Pod Lead - Material Science

Turing, a rapidly expanding leader in artificial intelligence, is advancing the development and deployment of cutting-edge AI systems. The company collaborates with top AI research labs to push the boundaries of model capabilities in areas such as reasoning, coding, multilinguality, and STEM knowledge. These advancements are then applied to create practical AI solutions addressing critical challenges faced by businesses worldwide.

Key Responsibilities:
Lead and mentor a team of 5 to 10 technical trainers focused on developing realistic, terminal-based scientific tasks within a frontier AI training and evaluation initiative. Oversee the creation and review of materials science tasks to ensure technical accuracy, reproducibility, and high-quality evaluation standards. Validate tasks that involve complex computational materials science methods including density functional theory (DFT), molecular dynamics, computational thermodynamics, phase diagrams, crystallography, and materials characterization. Ensure that tasks simulate authentic research workflows requiring multi-step scientific reasoning rather than simple data retrieval or library calls. Verify scientific correctness across various parameters such as unit consistency, crystal symmetry, convergence criteria, and simulation settings. Confirm that computational environments, software tools, pseudopotentials, and interatomic potentials are properly configured to guarantee reproducible outcomes. Review automated grading systems to ensure they assess meaningful scientific metrics like lattice parameters, formation energies, band gaps, elastic constants, and diffusion coefficients. Identify and address technical inaccuracies, unrealistic assumptions, and potential shortcuts that could undermine scientific rigor. Provide clear, actionable feedback to trainers, track revisions, and maintain quality control before task approval. Manage onboarding, task allocation, quality monitoring, and escalation of blockers while maintaining comprehensive technical documentation and best practices.

Required Qualifications:
Ph.D., postdoctoral experience, or equivalent advanced expertise in Materials Science, Metallurgy, or a closely related field. Proficiency in programming languages such as Python, C/C++, Fortran, or Julia, along with strong skills in Linux and terminal-based environments. Hands-on experience in at least one core 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 processes. Experience critically reviewing complex technical work, including scientific code, simulations, manuscripts, or research outputs. Ability to evaluate scientific assumptions, numerical accuracy, simulation convergence, and reliability of computational results. Strong analytical judgment to detect technical errors, edge cases, and flawed scientific assumptions. Experience mentoring 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 software 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 AI training, scientific quality control, benchmark development, or evaluation programs. Industry experience in semiconductors, batteries, alloys, advanced materials, or manufacturing. Contributions to publications, research, or open-source projects in computational materials science.

Offer Details:
This contractor role requires a commitment of at least 4 hours per day and a minimum of 40 hours per week, with a mandatory 4-hour overlap with Pacific Standard Time (PST). The contract duration is 4 weeks, with an expected start date in the near term. Please note that this is a contractor assignment without medical benefits or paid leave.