Remote Pod Lead - Material Science

Turing, a rapidly growing leader in AI innovation, 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, multilingual understanding, and STEM knowledge. These advancements are then applied to create real-world AI solutions that address critical business challenges.

Key Responsibilities:
Lead a team of 5 to 10 technical trainers focused on creating realistic, terminal-based scientific tasks for a frontier AI training and evaluation program. Oversee the development and review of materials science tasks to ensure technical accuracy, reproducibility, and high evaluation standards. Tasks must authentically represent computational materials research workflows and require multi-step scientific reasoning beyond simple data retrieval. Mentor trainers, maintain quality standards, and ensure consistent delivery of challenging scientific content. Review all aspects of scientific tasks, including problem statements, crystal structures, interatomic potentials, thermodynamic databases, simulation inputs, reference solutions, and automated grading systems. Validate tasks involving density functional theory (DFT), molecular dynamics, computational thermodynamics, phase diagrams, crystallography, materials characterization, mechanical properties, semiconductors, and energy materials. Confirm scientific correctness in units, crystal symmetry, convergence criteria, electronic structure calculations, thermodynamic consistency, and simulation parameters. Verify computational environments, scientific software, pseudopotentials, interatomic potentials, and dependencies to guarantee reproducible results. Critically assess 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 potential shortcuts that undermine scientific rigor. Provide clear, actionable technical feedback, track revisions, and ensure all quality issues are resolved before task approval. Manage trainer onboarding, work allocation, quality monitoring, throughput, escalation of blockers, and maintenance of technical documentation and best practices.

Required Qualifications:
Ph.D., postdoctoral experience, or equivalent advanced 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 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. Proven 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 detect 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. Contributions to publications, research, or open-source projects in computational materials science. Prior involvement in AI training, scientific quality control, benchmark development, or evaluation programs.

Offer Details:
This is a contractor role requiring a minimum commitment of 40 hours per week, with at least 4 hours of daily 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.