Material Sciences - Graduates - AI Training - Las Vegas

Material Sciences - Graduates - AI Training - US

Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world.

Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.

We're looking for Materials Science Specialists to join our Expert Network to help train and evaluate cutting‑edge AI models using real scientific expertise. If you have a background in materials research, engineering, or technical analysis, we'll send you a quick 10‑ to 15‑minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models.

Researchers looking for your skills tend to pay $40+/hr
, depending on skills and experience level. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.

• Educational Background: at minimum, a BSc (Bachelor of Science) in Materials Science, Metallurgy, Chemical Engineering, or a closely related STEM field.
• Technical Literacy: deep interest or experience in materials research, including the ability to interpret phase diagrams, crystalline structures, and property data.
• Analytical Rigor: strong attention to detail and the ability to concentrate on complex technical tasks for extended periods.
• Professional Verification: a valid Linked In profile or professional history to verify your credentials during the screening process.
• A Pay Pal account to receive payment from our clients.

• AI Evaluation & Ranking: comparing multiple AI-generated technical explanations or research summaries and ranking them based on scientific accuracy and logic.
• Strategic Document Audit: reviewing Materials Science scientific papers alongside AI-generated graphical abstracts to identify inaccuracies or misrepresentations.
• Fact‑Checking: verifying that the AI-generated data (formulas, material properties, and experimental results) faithfully represent the source scientific documentation.
• Model Alignment: providing high-level feedback to help AI models produce "enterprise‑ready" corporate and scientific intelligence.
• Technical Verification: auditing the structural integrity of summaries involving complex chemical compositions and material behaviors.

• Generative AI (LLMs): training and fine‑tuning models specialized in STEM and corporate intelligence.
• Scientific Visualization: evaluating AI-generated graphical abstracts and data visualizations.
• Research Databases: navigating technical literature to provide "ground truth" data.