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Materials Science Expert Remote

YO AI Labs


Job Location:

Los Angeles, CA - USA

Hourly Salary: $ 80 - 130
Posted: 16 September 2026 (8 hours ago)
Application Deadline: 14 December 2026
Vacancies: 1 Vacancy

Job Summary

Materials Science Expert

Role Type: Contractor
Location: Global Fully Remote
Schedule: 15 hours/week flexible

About the Role

Were seeking a Materials Science Expert to support an AI training project involving computational materials science materials modeling scientific simulation and Python. Youll solve validate and review materials-engineering tasks and create reproducible programmatic solutions. No prior AI experience is required.

Key Responsibilities
  • Solve and validate computational materials science problems.
  • Build material structures compositions and simulation inputs.
  • Run and analyze atomistic electronic molecular-dynamics continuum or electrochemical simulations.
  • Use Python to automate simulations parameter sweeps and data analysis.
  • Analyze mechanical thermal electrical chemical and structural properties.
  • Diagnose convergence numerical modeling and physical-assumption issues.
  • Compare results with experiments literature and expected physical trends.
  • Review AI-generated solutions and develop reliable reference solutions.
Required Skills
  • Materials Science & Engineering
  • Computational Materials Modeling
  • Materials Simulation & Analysis
  • Python
  • Scientific Computing
  • StructureProperty Relationships
  • Numerical Validation & Troubleshooting
  • Technical Problem-Solving
Tools

Experience with tools such as LAMMPS ASE pymatgen Quantum ESPRESSO FEniCSx CalculiX Elmer PyBaMM or equivalent CLI/API-based software. Familiarity with NumPy SciPy pandas Matplotlib or Jupyter is beneficial.

Qualifications
  • MS/PhD in Materials Science Metallurgy or related field; or Mechanical/Chemical Engineering with strong materials specialization.
  • Experience in computational materials modeling simulation characterization or materials R&D.
  • Strong Python and programmatic engineering-tool experience.
  • Ability to explain modeling assumptions validate results and distinguish computational errors from physical behavior.