What if your deep expertise in physics could directly shape how AI understands the physical world — ensuring it never violates conservation of energy, misapplies quantum mechanics, or hallucinates impossible thermodynamic processes?
We're looking for PhD-level Applied Physicists to challenge and evaluate cutting-edge Large Language Models on their understanding of fundamental physics. You'll design problems that push AI reasoning to its limits, author rigorous solutions, and document exactly where and how these models fail — work that directly influences how the next generation of AI is trained.
This is a fully remote, versátil contract role built for researchers and specialists who want to do meaningful, high-impact work on their own schedule.
Design Advanced Physics Problems — Craft open-ended, multi-step problems at PhD qualifying exam level, spanning quantum mechanics, electrodynamics, thermodynamics,
and classical mechanics
Author Ground-Truth Solutions — Write rigorous, step-by-step "golden responses" with precise units, constants, and mathematical derivations that serve as definitive benchmarks
Audit AI Reasoning — Evaluate AI-generated proofs, simulations, and derivations for physical consistency — identifying where models hallucinate physics that violates first principles
Refine Model Behavior — Provide structured, expert feedback to improve AI reasoning around boundary conditions, conservation laws, and physically constrained systems
Advance the Benchmark — Contribute to university-level and research-level evaluation frameworks that define how well AI truly understands the physical universe
Who You Are
Holds a PhD (completed or in final stages) in Applied Physics, Physics, Engineering Physics, or a closely related field
Deep mastery of the core pillars: Classical Mechanics, Electrodynamic
📌 Applied Physics Specialist (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.