We're looking for physics graduate students and degree holders to help train and evaluate cutting-edge AI models. Working with Alignerr — a partner to the world's leading AI research labs — you'll apply your advanced physics knowledge to problems that directly shape how the next generation of AI reasons about the physical world.
This is a fully remote, versátil contract role. No prior AI experience needed — your physics expertise is what matters.
Design challenging, graduate-level physics problems across domains including mechanics, electromagnetism, thermodynamics, and quantum physics
Develop clear, rigorous step-by-step solutions that demonstrate strong physical reasoning
Evaluate AI-generated responses for scientific accuracy, logical consistency, and quality of reasoning
Collaborate with researchers to build and refine benchmarks spanning undergraduate through Masters-level physics
Provide structured,
actionable feedback that helps AI systems improve over time
Who You Are
Currently pursuing or have completed a Masters degree in Physics, Applied Physics, or a closely related field
Strong command of advanced physics topics and the ability to reason through complex problems systematically
Able to communicate technical concepts clearly and precisely in writing
Detail-oriented and comfortable providing structured, evidence-based feedback
Self-motivated and able to work independently in a remote, async environment
No prior AI or data annotation experience required
Nice to Have
Experience with data annotation, data quality review, or evaluation systems
Familiarity with AI language models or benchmark design
Background spanning multiple physics subfields
Why Join Us
Work on meaningful, intellectually engaging problems at the frontier of AI development
Fully remote and asynchronous — work when and wh
📌 Physics Masters (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.