# Materials Science Expert - Remote at YO AI Labs in…

> Materials Science Expert - Remote at YO AI Labs in Seattle, Washington, United States. Category: Operations Jobs. View full job details and apply now on…

Materials Science Expert - Remote

# Materials Science Expert - Remote

YO AI Labs

## Job Description

Role Type: ContractorLocation: Global, Fully RemoteSchedule: 15 hours/week, flexible

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.

- 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.

- Materials Science & Engineering

- Computational Materials Modeling

- Materials Simulation & Analysis

- Python

- Scientific Computing

- Structure-Property Relationships

- Numerical Validation & Troubleshooting

- Technical Problem-Solving

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.

- 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.

Posted October 25, 2026

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