About Turing:

Turing is one of the worlds fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems.

Turing helps customers in two ways: Working with the worlds leading AI labs to advance frontier model capabilities in thinking reasoning coding agentic behavior multimodality multilinguality STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.

Role Overview:

- We are seeking an experienced Materials Science expert to lead a team of technical trainers developing realistic terminal-based scientific tasks for a frontier AI training and evaluation initiative.
- In this role you will oversee a pod of approximately 510 trainers review materials science tasks for technical accuracy reproducibility and evaluation quality and ensure tasks reflect authentic computational materials research workflows. You will also mentor trainers maintain quality standards and drive consistent delivery of challenging scientific tasks.

What youll do:

- 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 escalate blockers and maintain technical documentation and best practices.

What were looking for:

- 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 accurate technical feedback and documenting quality standards.

Nice to have:

- Experience with computational materials science tools such as Quantum ESPRESSO VASP GPAW ABINIT LAMMPS pymatgen ASE spglib phonopy pycalphad MOOSE FiPy OVITO or DREAM3D.
- Experience with machine-learned interatomic potentials high-throughput materials simulations or materials databases such as Materials Project.
- Familiarity with 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.
- Experience 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 experience in AI training scientific quality control benchmark development or evaluation programs.

Offer Details:

- Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST.
- Employment type : Contractor assignment (no medical/paid leave)
- Duration of contract : 4 week expected start date is next week

📌 Remote Pod Lead Material Science (Bengaluru)
🏢 Turing
📍 Bengaluru