Remote Materials Science Pod Lead for AI Training

Turing Enterprises, Inc.; Fatih; Remote; TRY 900,000 - 1,300,000; Full time

Benefits offered by this job

Contractor assignment (no medical/paid

Job summary

Turing Enterprises, Inc. seeks an experienced Materials Science expert to lead a pod of technical trainers developing terminal-based scientific tasks for AI training and evaluation.

You will review tasks for accuracy, mentor trainers, and ensure high-quality, authentic workflows across DFT, MD, and thermodynamics. The role requires a PhD or equivalent, strong programming skills, and experience in atomistic simulation or electronic-structure calculations.

Qualifications

• 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.
• Strong understanding of materials science fundamentals, numerical methods, computational modeling, and scientific validation.
• Experience reviewing complex technical work, scientific code, computational simulations, manuscripts, or research outputs.
• Ability to evaluate scientific assumptions, numerical accuracy, simulation convergence, and the reliability of computational results.
• Strong analytical judgment with the ability 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 providing precise technical feedback and documenting quality standards.

Responsibilities

• Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of materials science tasks.
• Review scientific tasks end to end, including 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 materials research workflows and require meaningful multi-step scientific reasoning rather than 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 ensure reproducible results.
• Review 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 opportunities to bypass scientific calculations or simulations.
• Provide clear, actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
• Onboard trainers, allocate work, monitor quality and throughput, elevate blockers, and maintain technical documentation and best practices.

Skills

Python

C/C++

Fortran

Julia

Linux/CLI

Materials modeling

Mentoring

Education

PhD in Materials Science

Tools

Quantum ESPRESSO

VASP

LAMMPS

pymatgen

ASE

spglib

Job description

Turing Enterprises, Inc. seeks an experienced Materials Science expert to lead a pod of technical trainers developing terminal-based scientific tasks for AI training and evaluation.

You will review tasks for accuracy, mentor trainers, and ensure high-quality, authentic workflows across DFT, MD, and thermodynamics. The role requires a PhD or equivalent, strong programming skills, and experience in atomistic simulation or electronic-structure calculations.