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 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 real-world AI solutions that address critical challenges faced by businesses.

Key Responsibilities:
Lead and mentor a team of 5–10 technical trainers focused on developing realistic, terminal-based scientific tasks for a frontier AI training and evaluation program. Oversee the review of materials science tasks to ensure technical accuracy, reproducibility, and high-quality evaluation standards. Validate tasks involving complex computational materials science workflows, including density functional theory (DFT), molecular dynamics, computational thermodynamics, phase diagrams, crystallography, and materials characterization. Ensure tasks require meaningful multi-step scientific reasoning rather than simple data retrieval or library calls. Verify scientific correctness across multiple parameters such as units, crystal symmetry, convergence criteria, and thermodynamic consistency. Validate computational environments, scientific software, pseudopotentials, and dependencies to guarantee reproducible results. Review automated grading systems to confirm they assess meaningful scientific outputs like lattice parameters, formation energies, band gaps, elastic constants, phonon properties, and diffusion coefficients. Identify and address technical inaccuracies, unrealistic assumptions, and potential shortcuts that bypass scientific rigor. Provide clear, actionable feedback to trainers, track revisions, and ensure all quality issues are resolved before task approval. Manage onboarding, work allocation, quality monitoring, and escalation of blockers while maintaining 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 familiarity with Linux and terminal-based environments. Hands-on experience 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 skills to identify 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 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 large language model (LLM)-generated scientific solutions. Industry experience in semiconductors, batteries, alloys, advanced materials, or manufacturing is a plus. 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.

The position requires 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). This is a contractor assignment without medical or paid leave benefits. The initial contract duration is 4 weeks, with an expected start date as early as next week.