Applied Physics

Applied Physics — AI Data Trainer
About the Role
What if your deep expertise in physics could directly shape how AI understands the physical world? We're looking for PhD-level Applied Physicists to stress-test cutting-edge Large Language Models — exposing the gaps in their reasoning and helping ensure they never violate the fundamental laws that govern reality.
This is a fully remote, flexible contract role built for researchers and scientists who want to do meaningful, intellectually rigorous 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

• Commitment : 10–40 hours/week

• Design advanced physics problems — craft open-ended, multi-step challenges at PhD qualifying exam level, spanning quantum mechanics, electrodynamics, thermodynamics, and classical mechanics

• Author rigorous ground-truth solutions — produce step-by-step "golden responses" with exacting precision across every constant, unit, and derivation

• Audit AI-generated physics — evaluate model outputs for physical consistency, identifying where AI "hallucinates" results that violate first principles

• Refine model reasoning — provide structured, expert feedback that helps AI systems better handle boundary conditions, conservation laws, and physics-informed constraints

- Work independently and asynchronously — fully on your own schedule

• Work independently and asynchronously — fully on your own schedule

• Holds a 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, scientific notation, and logical proof structure

• Self-motivated and reliable when working independently

- No prior AI or data annotation experience required

• No prior AI or data annotation experience required

• Prior experience with data annotation, dataset evaluation, or scientific quality assurance

• Proficiency with computational tools such as Python (NumPy/SciPy), MATLAB, or COMSOL

• Background in research-level benchmarking or academic problem design

- Familiarity with AI or LLM evaluation workflows

• Familiarity with AI or LLM evaluation workflows

• Work on cutting-edge AI projects alongside leading AI research labs

• Fully remote and flexible — work when and where it suits you

• Freelance autonomy with the structure of meaningful, high-impact work

• Apply your physics expertise to one of the most consequential challenges in modern AI

• Potential for ongoing work and contract extension as new projects launch