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Materials Science Expert – Remote NEW

New York, New York
YO AI Labs
Job Description Job Description Materials Science Expert

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

About the Role

We’re seeking a Materials Science Expert to support an AI training project involving computational materials science, materials modeling, scientific simulation, and Python. You’ll 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
  • Structure-Property 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.

Job Overview