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
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