Remote Applied Physicist for AI Reasoning & Physics Mastery

Alignerr Corp.

United States

Remote

AUD 118,000 - 216,000

Part time

14 hours ago
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Benefits offered by this job

Fully remote
Flexible hours

Job summary

Alignerr seeks PhD-level Applied Physicists to stress-test AI models and expose reasoning gaps against fundamental physical laws. This fully remote hourly contract emphasizes crafting advanced physics problems and golden solutions across quantum mechanics, electromagnetism, and thermodynamics.

You will audit AI proofs, refine model behavior with conservation laws, and document failure modes to guide future improvements while working independently at flexible hours.

Qualifications

  • PhD-level expertise in physics required or in final stages of completion
  • Rigorous mathematical derivations and clear communication of complex concepts
  • Experience crafting multi-step physics problems and solutions
  • Strong attention to units, notation and logical integrity of arguments

Responsibilities

  • Design PhD-level physics problems spanning quantum mechanics, electromagnetism and thermodynamics
  • Write rigorous, step-by-step golden solutions with precise constants and units
  • Audit AI reasoning for physical consistency and violations of conservation laws
  • Provide expert feedback to improve AI models with proper boundary conditions and symmetry arguments
  • Document failure modes to guide model improvement efforts

Skills

PhD in Applied Physics
Analytical writing
Independent work
Physics problem design

Education

PhD in Applied Physics or closely related field

Tools

Python (NumPy/SciPy)
MATLAB
COMSOL

Job description

Alignerr seeks PhD-level Applied Physicists to stress-test AI models and expose reasoning gaps against fundamental physical laws. This fully remote hourly contract emphasizes crafting advanced physics problems and golden solutions across quantum mechanics, electromagnetism, and thermodynamics.

You will audit AI proofs, refine model behavior with conservation laws, and document failure modes to guide future improvements while working independently at flexible hours.

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