Role Overview: We are seeking an experienced Materials Science expert to lead a team of technical trainers working on scientific tasks related to frontier AI training and evaluation. The role includes reviewing computational materials science tasks for scientific accuracy, reproducibility, and evaluation quality, while mentoring trainers and ensuring consistent delivery of high-quality technical work.
Requirements:
- Ph.D., postdoctoral experience, or equivalent advanced technical expertise in Materials Science, Metallurgy, or a related discipline.
- Strong programming skills in Python, C/C++, Fortran, or Julia, with proficiency in Linux and terminal-based environments.
- Hands-on experience in computational materials science, including atomistic simulations, density functional theory (DFT), molecular dynamics, computational thermodynamics, or electronic structure calculations.
- Strong understanding of scientific modeling, numerical methods, simulation validation, and reproducibility.
- Experience reviewing scientific code, computational simulations, research outputs, or complex technical work.
- Prior experience mentoring, reviewing, or leading technical or research teams.
- Excellent written communication skills and the ability to provide precise, actionable technical feedback.
Nice to Have:
- Experience with tools such as VASP, Quantum ESPRESSO, LAMMPS, ASE, pymatgen, or other computational materials science software.
- Familiarity with machine-learned interatomic potentials, materials databases, scientific benchmarks, or automated grading systems.
- Experience with Docker, Git, CI/CD, or high-performance computing (HPC) environments.
- Exposure to AI training, evaluation programs, or AI-generated scientific solutions.
- Research publications or industry experience in semiconductors, batteries, alloys, or advanced materials.
Project Details:
- Commitment: Full-time — minimum 40 hours/week, with at least 4 hours of overlap with PST.
- Duration: 3 weeks.
- Start Date: Expected to begin next week.
- Employment Type: Contractor assignment (no medical or paid leave).
- Role Responsibilities: Lead a pod of approximately 5–10 trainers, review scientific tasks and automated evaluations, ensure technical accuracy and reproducibility, and maintain quality standards.
