Applied Physics - AI Data Trainer

About the Role

What if your deep expertise in physics could directly shape how AI understands the fundamental laws of the universe? We're looking for PhD-level Applied Physicists to stress-test cutting-edge AI models - exposing gaps in their physical reasoning and helping ensure they never violate the principles of conservation of energy, momentum, or anything else your training tells you is non-negotiable. This is a fully remote, flexible contract role built for researchers and scientists who want high-impact work on their own schedule. No prior AI experience required - just a command of physics that goes all the way down to first principles. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : 10-40 hours/week

What You'll Do

Design Advanced Physics Problems - Craft open-ended, multi-step problems at PhD qualifying exam level, spanning quantum mechanics, electrodynamics, thermodynamics, and classical mechanics Author Gold-Standard Solutions - Write rigorous, step-by-step "golden responses" with flawless handling of physical constants, unit conversions, and mathematical derivations Audit AI Reasoning - Evaluate AI-generated proofs and simulations for physical consistency, identifying where models hallucinate results or violate first principles Refine Model Behavior - Provide structured, expert feedback that improves how AI handles boundary conditions, conservation laws, and physics-informed constraints Document Failure Modes - Systematically record where and how AI reasoning breaks down so research teams can address root causes Who You Are Completed or nearly completed PhD 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 clear, structured English Uncompromising precision with units, scientific notation, and the logical structure of proofs Self-directed and reliable - comfortable working independently on technical tasks without hand-holding No prior AI or machine learning experience required

Nice to Have

Experience with data annotation, scientific dataset evaluation, or quality assurance workflows Proficiency with tools like Python (NumPy/SciPy), MATLAB, or COMSOL Background in research publication, technical writing, or academic instruction

Why Join Us

Work on some of the most technically demanding AI projects in existence alongside world-leading research labs Fully remote and asynchronous - work when and where it suits you Freelance autonomy with the structure of meaningful, high-value technical work Rare opportunity to apply your physics expertise beyond academia in a high-impact, forward-looking field Potential for ongoing work and contract extension as new projects launch Experience: 3 years required