Pod lead - Material Science (Terminal Bench Science)

Requisitos

• 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

Desejáveis

• 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