Remote Applied Physicist - AI Physics QA (Contract)

Alignerr Corp.; Melbourne; Remote; AUD 120,000 - 240,000; Part time

Benefits offered by this job

Fully remote

Flexible hours

Freelance autonomy

Job summary

Alignerr is seeking PhD-level Applied Physicists to stress-test AI reasoning against physical laws in a fully remote, hourly contract. You will work 10–40 hours per week on complex physics problems spanning quantum mechanics, electrodynamics, and thermodynamics.

Responsibilities include crafting open-ended problems, authoring rigorous solutions with correct constants and units, auditing AI reasoning for physical consistency, and documenting failure modes for research teams.

Qualifications

• PhD or near completion in Applied Physics or related field.
• Deep mastery of core physics pillars (classical mechanics, electromagnetism, statistical mechanics, quantum mechanics).
• Ability to explain complex phenomena and derivations clearly.
• Attention to detail in notation, units, and logical flow.

Responsibilities

• Design advanced physics problems at PhD-level across QM, EM, CM, and thermodynamics.
• Author rigorous, step-by-step solutions with correct constants and units.
• Audit AI reasoning for physical consistency and boundary conditions.
• Provide expert feedback to refine AI model behaviour and conservation laws.
• Document failure modes of AI reasoning for research teams.

Skills

Quantum Mechanics

Electrodynamics

Classical Mechanics

Statistical Mechanics

Thermodynamics

Education

PhD completed or in final stages in Applied Physics, Physics, Engineering Physics

Tools

MATLAB

COMSOL

Python (NumPy/SciPy)

Job description

Alignerr is seeking PhD-level Applied Physicists to stress-test AI reasoning against physical laws in a fully remote, hourly contract. You will work 10–40 hours per week on complex physics problems spanning quantum mechanics, electrodynamics, and thermodynamics.

Responsibilities include crafting open-ended problems, authoring rigorous solutions with correct constants and units, auditing AI reasoning for physical consistency, and documenting failure modes for research teams.