Applied Physics

Applied Physics — AI Data TrainerAbout The RoleWhat 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: AlignerrType: Hourly ContractLocation: RemoteCommitment: 10–40 hours/weekWhat You'll DoDesign 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 thermodynamicsAuthor gold-standard solutions — Write definitive, step-by-step solutions with exacting attention to physical constants, unit conversions, and logical flowAudit AI reasoning — Evaluate AI-generated simulations, derivations, and proofs for physical consistency; identify where models "hallucinate" physics that violates first principlesRefine model behaviour — Provide structured, expert feedback that helps AI develop true physics-informed reasoning, including boundary conditions, symmetry constraints, and conservation lawsWho You AreHold a PhD (completed or near-completion) in Applied Physics, Physics, Engineering Physics, or a closely related fieldHave mastery across the core pillars: Classical Mechanics, Electrodynamics, Statistical Mechanics, and Quantum MechanicsWrite with exceptional clarity — able to explain complex physical phenomena and mathematical derivations in structured, precise EnglishObsessively detail-oriented when it comes to units, scientific notation, and the logical integrity of a proofSelf-directed and comfortable working independently on technical tasksNo prior AI or data annotation experience requiredNice to HaveExperience with scientific computing tools such as Python (NumPy/SciPy), MATLAB, or COMSOLPrior work in data annotation, dataset curation, or scientific evaluation systemsBackground in research involving simulation, modelling, or experimental physicsWhy Join UsWork on high-impact AI projects in collaboration with world-leading research labsFully remote and asynchronous — work on your own schedule, wherever you areFreelance autonomy with meaningful, intellectually stimulating workDirect exposure to frontier AI development and how large language models are trained on scientific reasoningPotential for ongoing work and contract extension as new projects launch