We are looking for an experienced Materials Science expert to lead a pod of approximately 5–10 technical trainers developing realistic, terminal-based scientific tasks for a frontier AI training and evaluation initiative.

The role involves reviewing computational materials science tasks for scientific accuracy, reproducibility, and evaluation quality while mentoring trainers and maintaining consistent delivery standards.

Key Responsibilities

- Lead and mentor technical trainers, ensuring quality and timely delivery.
- Review scientific tasks, 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, semiconductors, and energy materials.
- Verify scientific assumptions, units, crystal symmetry, convergence criteria, and simulation parameters.
- Review scientific software, pseudopotentials, dependencies, and computational environments.
- Validate graders for lattice parameters, formation energies, band gaps, elastic constants, diffusion coefficients, and related scientific outputs.
- Identify technical inaccuracies and unrealistic simulation assumptions.
- Provide detailed feedback, track revisions, and maintain technical documentation.
- Allocate work, monitor quality and throughput, and escalate blockers.

Required Qualifications

- PhD, postdoctoral experience, or equivalent advanced technical experience in Materials Science, Metallurgy, or a closely related discipline.
- Solid programming skills in Python, C/C++, Fortran, or Julia.




- Proficiency in Linux and terminal-based environments.
- Hands-on expertise in atomistic simulation, computational thermodynamics, mesoscale modeling, electronic structure calculations, or materials characterization data analysis.
- Experience reviewing scientific code, computational simulations, manuscripts, or research outputs.
- Strong understanding of numerical methods, computational modeling, and scientific validation.
- Experience mentoring or leading technical or research teams.
- Excellent written communication skills.

Preferred Qualifications

- Experience with Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, phonopy, or related tools.
- Experience with machine-learned interatomic potentials or high-throughput materials simulations.
- Familiarity with Docker, Git, CI/CD, automated testing, and HPC environments.
- Experience developing scientific benchmarks or evaluating AI-generated scientific solutions.
- Industry experience in semiconductors, batteries, alloys, advanced materials, or manufacturing.

Contract Details

- Duration: 4 weeks.
- Minimum commitment: 40 hours per week.
- Daily commitment: At least 8 hours.
- Required overlap: 4 hours with PST.
- Contractor assignment; medical and paid leave are not provided.

Application Instructions: Submit your updated resume highlighting your computational materials science expertise, scientific programming, simulation experience, and team leadership.

Pay: Up to ₹1,500,000.00 per year

Application Question(s):

- Do you hold a PhD, have postdoctoral experience, or possess equivalent advanced experience in Materials Science, Metallurgy, or a related field?

Work Location: Remote

📌 Pod Lead – Materials Science (India)
🏢 Destinare Services
📍 India