Applied Physics Specialist
About this role
About the Role What if your years of physics training could directly shape how AI understands the physical world? We're looking for PhD-level Applied Physicists to put cutting-edge Large Language Models through their paces — designing rigorous problems, exposing flawed reasoning, and helping ensure that AI respects the fundamental laws of the universe. This is a fully remote, flexible contract role built for working researchers and academics. No prior AI experience needed — just deep, hard-earned expertise in physics and a sharp eye for where reasoning breaks down. - Organization: Alignerr - Type: Hourly Contract - Location: Remote - Commitment: 10–40 hours/week What You'll Do - Design Advanced Physics Problems — Craft PhD qualifying exam-level problems spanning quantum mechanics, electromagnetism, thermodynamics, and classical mechanics that require multi-step reasoning and rigorous mathematical derivation - Author Ground-Truth Solutions — Produce precise, step-by-step "golden responses" where every physical constant, unit conversion, and logical step is airtight - Audit AI Reasoning — Evaluate AI-generated solutions and proofs for physical consistency, identifying where models "hallucinate" physics that violates first principles - Refine Model Behaviour — Provide structured, expert feedback that improves AI reasoning around boundary conditions, conservation laws, and physics-informed constraints - Document Failure Modes — Systematically catalogue how and why AI models break down on research-level physics benchmarks 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 communicate complex physical phenomena and mathematical derivations in clear, structured prose - Uncompromising precision with units, scientific notation, dimensional analysis, and logical proof structure - Self-directed and reliable when working independently on task-based assignments - No prior AI or data annotation experience required Nice to Have - Experience with data annotation, scientific dataset evaluation, or quality assurance workflows - Proficiency with computational tools such as Python (NumPy/SciPy), MATLAB, or COMSOL - Background in writing research papers, problem sets, or technical documentation - Familiarity with AI/ML concepts or large language model behaviour Why Join Us - Work directly with leading AI research labs on genuinely high-impact projects - Fully remote and flexible — structure your hours around your existing research or academic commitments - Freelance autonomy: choose your workload, work asynchronously, and collaborate globally - Gain rare, first-hand exposure to how frontier AI models are trained and evaluated - Meaningful work that ensures AI reasoning is grounded in real physics — not plausible-sounding nonsense - Potential for ongoing work and contract extension as new projects launch
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