Applied Physics

About The Role

What if your deep understanding of the physical world could directly shape how AI reasons about reality? We're looking for PhD-level Applied Physicists to stress-test cutting-edge AI models — exposing the gaps in their understanding of quantum mechanics, electromagnetism, thermodynamics, and more. This is a fully remote, flexible contract role. No prior AI experience needed — just mastery of physics and the ability to think rigorously about how the universe works.

Organization

Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week

What You'll Do

• Design Advanced Problems — Craft PhD-level, open-ended physics problems that demand multi-step logical reasoning and rigorous mathematical derivation across domains like quantum mechanics, electrodynamics, and statistical mechanics
• Author Ground-Truth Solutions — Develop precise, step-by-step "golden responses" where every constant, unit conversion, and logical step is airtight
• Audit AI Reasoning — Evaluate AI-generated proofs and simulations for physical consistency, identifying where models "hallucinate" physics that violates first principles
• Refined AI Thinking — Provide structured, expert feedback to improve how AI models handle boundary conditions, conservation laws, and physics-informed constraints
• Document Failure Modes — Systematically record how and where AI reasoning breaks down so models can be corrected and improved at a foundational level

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 analytical writing skills — you can explain complex derivations clearly and precisely in structured English
• Uncompromising attention to detail when it comes to units, notation, and the logical flow of a proof
• Self-motivated and able to work independently without hand-holding
• No prior AI or machine learning experience required

Nice to Have

• Experience with data annotation, dataset quality review, or scientific evaluation systems
• Proficiency with computational tools such as Python (NumPy/SciPy), MATLAB, or COMSOL
• Background in research publishing or technical writing
• Familiarity with AI tools or large language models as an end user