Computational Materials Science AI Training Expert

You will create, solve, review, and validate computational materials science and materials engineering tasks. The work combines materials modeling, scientific simulation, Python automation, and expert review of AI-generated solutions.

You will assess whether results can be reproduced, are numerically valid, and make physical sense across material structures, properties, processing, performance, and failure.

• Build material structures, atomic configurations, compositions, and inputs ready for scientific solvers.
• Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, and related simulations.
• Use Python to create inputs, automate calculations, run parameter sweeps, process results, and validate outputs.
• Analyze mechanical, thermal, electrical, chemical, structural, and electrochemical properties.
• Diagnose failed calculations, invalid structures, convergence problems, numerical instability, and incorrect physical assumptions.
• Compare results with experimental data, literature values, known properties, and expected physical trends.
• Review AI-generated solutions for scientific correctness and create reproducible reference solutions with objective verification methods.

What It Pays And Takes

This is a fully remote contractor opportunity with a flexible schedule. The role is listed as entry level, but the work requires advanced materials knowledge and practical experience with computational modeling or related engineering analysis.

• Pay: $80 to $150 per hour equivalent, paid per task that meets the project specifications.
• Schedule: Approximately 15 hours per week with flexible timing.
• Location: Fully remote and open to candidates in the countries specified for this role.
• Language: Professional fluency in English.
• Education: An MS or PhD in materials science and engineering, metallurgy, mechanical engineering with substantial materials specialization, chemical engineering with substantial materials expertise, or a closely related discipline is expected.
• Materials expertise: Advanced understanding of materials behavior and structure-property relationships.
• Technical skills: Practical proficiency with Python for scientific or engineering work.
• Experience: Computational materials modeling, simulation, characterization, or materials-focused engineering analysis.
• Tools: Experience using at least one engineering or scientific tool through a command-line interface, scripting interface, configuration files, or programmatic API.
• Technical judgment: Ability to justify modeling assumptions, parameters, approximations, and convergence criteria; separate computational failures from physical behavior; and explain technical limits clearly.
• Relevant backgrounds may include academic research, national laboratories, industry research and development, computational engineering, or other demonstrated materials work.
• Useful tools include LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, NumPy, SciPy, pandas, Matplotlib, Jupyter, or comparable scientific software.
• Exclusive experience with graphical user interfaces is not sufficient because solutions must be reproducible through code, scripts, configuration files, or command-line tools.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind systems that learn from examples, including creating technical problems, checking model answers, and rating whether solutions are correct. OpenTrain connects contributors with this work, and experienced specialists are needed when tasks require sound scientific reasoning and reproducible results.