Hello, Greetings from ZettaMine Freelancing Opportunity :
- We are hiring a Pod Lead – Materials Science (Terminal Bench Science) for a short-term Frontier AI Research, Scientific Computing & Engineering Evaluation project across the following locations: India Role: Pod Lead – Materials Science (Terminal Bench Science) Educational Eligibility: PhD, postdoctoral experience, or equivalent advanced technical experience in Materials Science, Metallurgy, or a closely related discipline. Mode: Remote Start: Immediate Mandatory Eligibility Criteria Technical Qualification:
• PhD, 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.
• Solid 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. Availability:
• Minimum 40 hours per week.




• Availability to work at least 8 hours per day.
• Minimum 4 hours of overlap with PST business hours. What You'll Work On:
• Lead and mentor a pod of approximately 5–10 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. 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. Interested candidates: Please share your updated CV and LinkedIn profile at [email protected] Thanks & Regards, Sowmya P

📌 Pod lead (India)
🏢 ZettaMine Labs
📍 India