Physics Quality Assurance Lead

The Work

As a Physics Quality Assurance Lead, you will review AI-generated physics content and trainer QA work across physics training projects. You will assess whether the work is scientifically correct, follows project rubrics, and explains ideas clearly.

• Review physics explanations, calculations, diagrams, derivations, experimental interpretations, and step-by-step reasoning.
• Check scientific accuracy, physical reasoning, calculation correctness, units, formulas, conceptual clarity, formatting, and instruction-following.
• Flag incorrect assumptions, wrong formulas, unit errors, flawed reasoning, sign convention mistakes, impossible claims, and misleading explanations.
• Give precise written feedback that helps contributors meet project guidelines and quality standards.
• Identify recurring quality issues and coordinate workflow updates with trainers and other QA contributors.
• Maintain style guides, FAQs, trackers, examples, honeypots, onboarding materials, and other QA documentation.
• Support contributor onboarding, run training calls for physics contributors, and follow up on availability and activation issues.

What It Pays and Takes

This is a remote, part-time contractor role for candidates hired in the United States. The work requires strong physics knowledge and the ability to provide clear written feedback in English.

• Pay: Up to $75 per hour.
• Time: 20 or more hours per week.
• Work arrangement: Remote contractor role with part-time employment.
• Location: U.S. hiring only.
• Education: A degree in physics, applied physics, engineering physics, astrophysics, mathematics, engineering, or a closely related quantitative field.
• Experience: At least 3 years in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or related scientific work.
• Knowledge: Classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling.
• Communication: Strong English writing for feedback and team coordination.
• Helpful experience: AI training, data annotation, large language model evaluation, scientific QA, academic review, or rubric-based review.
• Additional helpful background: Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, numerical methods, and remote team support.
• Tools and team experience: Discord, Google Sheets, Google Docs, trackers, dashboards, project management systems, or teams of educators, reviewers, researchers, annotators, science writers, or QAs.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind systems that generate and evaluate information. Specialists review examples, check reasoning, and provide feedback so AI models learn to produce more accurate and useful answers.