Physics Quality Assurance Lead (QAL)

About this role

Physics Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across physics AI training projects. You will review AI-generated physics content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards.

AI Training for Physics experts

Responsibilities

• Spot-check physics items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• Review AI-generated physics explanations, calculations, diagrams, derivations, experimental interpretations, and step-by-step reasoning.
• Update trainers/QAs on Discord about guidelines, workflow updates, and physics-specific quality expectations.
• Respond to questions around physical assumptions, formulas, units, derivations, diagrams, experimental setups, and rubric interpretation.

Qualifications

• Bachelor’s, Master’s, or PhD degree in Physics, Applied Physics, Engineering Physics, Astrophysics, Mathematics, Engineering, or a closely related quantitative/scientific field.
• Strong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of experience in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or related scientific workflows.
• Strong understanding of classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling.
• Ability to evaluate physics content against rubrics and identify issues such as incorrect assumptions, wrong formulas, unit errors, flawed reasoning, sign convention mistakes, physically impossible claims, or misleading explanations.
• Familiarity with tools or methods such as Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, and numerical methods is preferred.