Computational Materials Science AI Training Expert

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. As the hiring and contracting organization for this role, OpenTrain connects specialized experts with meaningful work that helps improve advanced AI systems while supporting a durable professional portfolio.

• Build a profile that showcases your AI-training and technical experience
• Discover specialized projects that match your expertise
• Apply in minutes with a free OpenTrain account

About AI Training Work

AI training is the human side of building artificial intelligence. Experts create examples, solve technical problems, and review model-generated work so AI systems can produce more accurate, reliable, and useful results. This role focuses on evaluating scientific reasoning, simulations, code, and computational results in materials science.

• Contribute to the development of cutting-edge AI systems
• Use your professional expertise in flexible, fully remote work
• Help improve the accuracy and reproducibility of AI-generated scientific solutions

The Role

OpenTrain is seeking a Computational Materials Science AI Training Expert to create, solve, review, and validate computational materials science and materials engineering tasks. The work combines materials modeling, scientific simulation, Python automation, and expert evaluation of AI-generated solutions.

You will assess whether computational results are reproducible, numerically valid, and physically meaningful, with attention to material structures, properties, processing, performance, and failure. The opportunity is fully remote and open globally as a part-time contractor engagement.

• Schedule: Approximately 15 hours per week
• Work arrangement: Fully remote, worldwide
• Engagement: Part-time contractor
• Compensation: $80 to $130 per hour
• Working language: English

What You'll Do

You will develop reproducible computational tasks and reference solutions, then evaluate AI-generated approaches for scientific correctness. Your work will span model setup, simulation execution, result analysis, troubleshooting, and objective verification.

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

Required Qualifications

This role is classified as entry level in the listing, but it requires graduate-level subject expertise and practical computational experience. You should be comfortable defending technical decisions and explaining scientific limitations clearly.

• Advanced understanding of materials behavior and structure-property relationships
• Practical proficiency with Python for scientific or engineering work
• Experience with computational materials modeling, simulation, characterization, or materials-focused engineering analysis
• Experience operating at least one engineering or scientific tool through a command-line interface, scripting interface, configuration files, or programmatic API
• Ability to justify modeling assumptions, parameters, approximations, and convergence criteria
• Ability to distinguish computational failures from genuine physical behavior
• Ability to evaluate convergence, numerical stability, physical plausibility, and AI-generated scientific solutions

Education and Useful Tools

An MS or PhD is expected in Materials Science and Engineering, Metallurgy, Mechanical Engineering with a substantial materials specialization, Chemical Engineering with substantial materials expertise, or a closely related discipline. Relevant experience may come from 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 programmatic scientific software. Exclusive experience with graphical user interfaces is not sufficient because task solutions must be reproducible through code, scripts, configuration files, or command-line tools.

• Graduate-level expertise in materials science, metallurgy, or a closely related engineering discipline
• Experience with scientific simulation or computational materials modeling
• Experience using practical Python automation in scientific or engineering workflows
• Familiarity with at least one scriptable, CLI-accessible, or programmatic engineering tool

Why Work With OpenTrain

OpenTrain helps experts build careers in the fast-growing AI-training industry. Your materials science knowledge can directly shape how AI systems reason about simulations, engineering analysis, and scientific evidence, while flexible remote work lets you contribute around your existing commitments.

• Work from anywhere with an internet-connected computer
• Choose a flexible part-time schedule within the project requirement
• Turn specialized AI-training work into a lasting professional portfolio
• Find and manage opportunities through one career-focused OpenTrain profile