Materials Science/Engineering Specialist - Freelance AI Trainer Project

PhilippinesOn-site

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Requirements

Are you a materials science expert eager to shape the future of AI? Large‑scale language models are evolving from clever chatbots into powerful engines of scientific discovery.
With high‑quality training data, tomorrow’s AI can democratize world‑class education, keep pace with cutting‑edge research, and streamline lab work for scientists everywhere.
That training data begins with you—we need your expertise to help power the next generation of AI.
You’ll challenge advanced language models on topics like phase transformations, microstructure-property relationships, materials characterization techniques (XRD, SEM, TEM), thermal analysis, additive manufacturing, materials failure analysis, and sustainability in materials engineering—documenting every failure mode so we can harden model reasoning.
A PhD in materials science, materials engineering, or a closely related physical science field is ideal; peer‑reviewed publications, lab-based testing, product design using advanced materials, or applied research signal fit.
Ready to turn your materials science expertise into the knowledge base for tomorrow’s AI? Apply today and start teaching the model that will teach the world.
Job title: Materials Science Specialist – AI Trainer

We offer a pay range of $8‑to‑$65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location.
Final offer amounts may vary from the pay range listed above.
benefits such as health insurance and PTO do not apply.

We’re looking for materials science and engineering specialists who live and breathe crystallography, thermomechanical processing, nanomaterials, polymer science, electronic materials, biomaterials, corrosion science, and composite development.
On a typical day, you will converse with the model on lab scenarios and theoretical materials science questions, verify factual accuracy and logical soundness, capture reproducible error traces, and suggest improvements to our prompt engineering and evaluation metrics.