What if your deep knowledge of semiconductor materials or molecular modeling could directly shape how AI reasons about the physical world? We're looking for Materials Science experts — Masters and PhD holders — to help train and evaluate cutting-edge AI models on some of the most complex problems in modern science.
This is a fully remote, versátil contract role built for researchers and domain specialists who want to put their expertise to work in a new and meaningful way. No prior AI experience needed — just rigorous scientific knowledge and a sharp analytical mind.
Develop, solve, and critically review advanced material science problems with real-world relevance
Apply your expertise in semiconductor materials, molecular modeling, or related domains to design complex, high-quality problem statements
Evaluate AI-generated scientific reasoning and outputs for accuracy, depth, and rigor
Collaborate asynchronously with AI researchers and fellow domain experts to push the boundaries of what AI can understand in your field
Ensure scientific clarity, precision, and depth across every deliverable you contribute
Who You Are
Holds a Master's or PhD in Materials Science, or a closely related discipline
Deep expertise in semiconductor materials, molecular modeling, or adjacent research areas
Comfortable coding in Python or MATLAB — whether from research, coursework, or applied projects
Exceptionally clear communicator in writing, with strong attention to scientific detail
Self-directed and reliable when working independently on an asynchronous schedule
Based in the U.S., Canada, U.K., Australia, or New Zealand
Nice to Have
Prior experience with data annotation, data quality review, or AI evaluation workflows
Background in academic publishing, peer review, or technical writing
Familiarity with computational mate
📌 Material Science Expert (Ciudad de México)
🏢 Alignerr
📍 Ciudad de México
Postulate a este anuncio
Muestra tus habilidades a la empresa, rellenar el formulario y deja un toque personal en la carta, ayudará el reclutador en la elección del candidato.