Pod lead - Material Science (Terminal Bench Science)

Hello, Greetings from ZettaMine!!! Freelancing Opportunity :- We are hiring a Pod Lead – Material Science (Terminal Bench Science) for a short-term Frontier AI Research, Computational Materials Science & AI Model Evaluation project across the following locations: Eligible Locations: 🇧🇩 Bangladesh | 🇧🇷 Brazil | 🇨🇴 Colombia | 🇪🇬 Egypt | 🇬🇭 Ghana | 🇮🇳 India | 🇮🇩 Indonesia | 🇳🇬 Nigeria | 🇹🇷 Turkey Role: Pod Lead – Material 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 Duration: 3 Weeks Engagement Type: Short-Term Contract (Contractor Assignment) Mandatory Eligibility Criteria Technical Qualification: PhD, postdoctoral experience, or equivalent advanced technical expertise in Materials Science, Metallurgy, or a closely related field. 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 | 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 skills to identify technical errors, unrealistic assumptions, edge cases, and flawed scientific methodologies. Experience mentoring technical teams, reviewing deliverables, or leading small research or engineering teams. Excellent written communication skills for providing precise technical feedback and documenting scientific requirements. Preferred Technical Skills: Experience with computational materials science tools such as Quantum ESPRESSO, VASP, GPAW, ABINIT, LAMMPS, pymatgen, ASE, spglib, phonopy, pycalphad, MOOSE, FiPy, OVITO, or DREAM3D. Knowledge of 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, simulation-based evaluations, programming challenges, or automated grading systems. Experience evaluating AI coding agents, terminal-based agents, or LLM-generated scientific solutions. Availability: Minimum 40 hours per week. Availability to work at least 8 hours per day, with a minimum commitment of 4 hours per day. Minimum 4 hours of overlap with PST business hours. Immediate availability to start the short-term contract assignment. What You’ll Work On: Lead and mentor a pod of approximately 5–10 technical trainers developing realistic, terminal-based materials science tasks for a frontier AI training and evaluation initiative. 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 authentic computational materials research workflows and require meaningful multi-step scientific reasoning rather than simple database lookups or library calls. Verify scientific accuracy 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 accurately evaluate 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 tasks, monitor quality and productivity, resolve technical blockers, and maintain consistent delivery standards. Maintain technical documentation, quality standards, and best practices throughout task development. Important Offer Details Commitment: Minimum 40 hours per week. Working Hours: 8 hours per day. PST Overlap: Minimum 4 hours. Contract Duration: 3 weeks. Interested Candidates: Please share your updated CV and LinkedIn profile at [redacted email]. Thanks & Regards, Vyshnavi E ZettaMine Show more Show less