Applied Physics — AI Data Trainer
About The Role
What if your deep expertise in physics could directly shape how AI understands the physical world — from quantum mechanics to thermodynamics to the fundamental laws that govern reality?
We're looking for PhD-level Applied Physicists to stress-test cutting-edge AI models, expose their reasoning failures, and help train them to think like a physicist. Your work will directly influence how the next generation of AI handles real scientific problems — and whether it respects the laws of the universe when it does.
This is a fully remote, flexible contract role. No prior AI experience required — just a mastery of physics and a sharp analytical mind.
- 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, requiring multi-step logical reasoning and rigorous mathematical derivation across areas like quantum mechanics, electrodynamics, and thermodynamics
- Author gold-standard solutions — Write definitive, step-by-step solutions with exacting attention to physical constants, unit conversions, and logical flow
- Audit AI reasoning — Evaluate AI-generated simulations, derivations, and proofs for physical consistency; identify where models "hallucinate" physics that violates first principles
- Refine model behaviour — Provide structured, expert feedback that helps AI develop true physics-informed reasoning, including boundary conditions, symmetry constraints, and conservation laws
Who You Are
- Hold a PhD (completed or near-completion) in Applied Physics, Physics, Engineering Physics, or a closely related field
- Have mastery across the core pillars: Classical Mechanics, Electrodynamics, Statistical Mechanics, and Quantum Mechanics
- Write with exceptional clarity — able to explain complex physical phenomena and mathematical derivations in structured, precise English
- Obsessively detail-oriented when it comes to units, scientific notation, and the logical integrity of a proof
- Self-directed and comfortable working independently on technical tasks
- No prior AI or data annotation experience required
Nice to Have
- Experience with scientific computing tools such as Python (NumPy/SciPy), MATLAB, or COMSOL
- Prior work in data annotation, dataset curation, or scientific evaluation systems
- Background in research involving simulation, modelling, or experimental physics
Why Join Us
- Work on high-impact AI projects in collaboration with world-leading research labs
- Fully remote and asynchronous — work on your own schedule, wherever you are
- Freelance autonomy with meaningful, intellectually stimulating work
- Direct exposure to frontier AI development and how large language models are trained on scientific reasoning
- Potential for ongoing work and contract extension as new projects launch
