Applied Physics (AI Training)

About The Role

What if your expertise in quantum mechanics, thermodynamics, and electrodynamics could directly shape how AI understands the physical world? We're looking for PhD-level Applied Physicists to stress-test cutting-edge AI models — exposing the gaps in their physical reasoning and helping ensure they respect the fundamental laws of the universe.

This is a fully remote, flexible contract role built for researchers and domain experts who want to apply their deep knowledge to one of the most consequential technology challenges of our time. No prior AI experience required — just rigorous scientific training and an uncompromising eye for physical truth.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Design Advanced Physics Problems — Craft complex, open-ended problems at PhD qualifying exam level that demand multi-step logical reasoning, mathematical derivation, and deep physical intuition
• Author Ground-Truth Solutions — Produce rigorous,



step-by-step "golden responses" with airtight unit analysis, precise constants, and logically sound derivations
• Audit AI Reasoning — Evaluate AI-generated proofs and simulations for physical consistency, identifying where models "hallucinate" physics that violates first principles like conservation of energy or momentum
• Refine AI Understanding — Provide structured, expert feedback that improves a model's ability to reason within physical constraints — from boundary conditions to thermodynamic limits

Who You Are

• PhD completed (or in final stages) in Applied Physics, Physics, Engineering Physics, or a closely related field
• Deep mastery across the core pillars: Classical Mechanics, Electrodynamics, Statistical Mechanics, and Quantum Mechanics
• Exceptional ability to explain complex physical phenomena and mathematical derivations in explicit, structured English
• Uncompromising precision with units, scientific notati

📌 Applied Physics (India)
🏢 Alignerr
📍 India