Applied Physics

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

Responsibilities

• 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

Requirements

• 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

Benefits

• 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