Physics Quality Assurance Lead

Role overview

This is a remote, hourly contract position for a Physics Quality Assurance Lead. In this role, you will oversee quality control, consistency, and trainer performance across physics-focused AI training projects. Your work will help ensure that physics data used for AI model training is accurate, scientifically sound, clearly explained, and aligned with project expectations.

The organization is an AI data services company that supports some of the world’s largest AI firms and foundation-model labs. You will review AI-generated physics content as well as trainer and QA outputs, checking them against project rubrics and quality standards. There is currently no active project for this position, but suitable candidates may be contacted first when relevant assignments become available. Qualified experts will also gain access to future opportunities through the company’s expert network.

What you will do

• Audit physics tasks, identify quality problems, and share clear written feedback with the relevant contributors.
• Review AI-generated answers, calculations, diagrams, derivations, experimental interpretations, and step-by-step solutions.
• Check work for scientific accuracy, correct physical reasoning, valid formulas, proper unit handling, dimensional consistency, logical clarity, and correct formatting.
• Look for errors such as incorrect assumptions, flawed derivations, sign mistakes, misleading explanations, impossible claims, or unsafe content.
• Escalate repeated or serious issues and help improve overall quality processes.
• Keep trainers and QAs informed about guideline changes, workflow updates, and physics-specific expectations through Discord and other tools.
• Answer questions about assumptions, formulas, derivations, diagrams, experimental setups, and rubric interpretation.
• Reach out to inactive contributors, encourage reactivation, track follow-ups, and note availability concerns.
• Build and maintain documentation such as style guides, FAQs, examples, trackers, honeypots, calibration tasks, and onboarding resources.
• Support and lead onboarding and training sessions for physics contributors.
• Identify recurring problem areas and help refine physics QA workflows.

Candidate profile

Applicants should have a bachelor’s, master’s, or PhD in Physics, Applied Physics, Engineering Physics, Astrophysics, Mathematics, Engineering, or another closely related quantitative or scientific discipline. Strong English communication skills are required for guideline comprehension, technical feedback, and team collaboration.

The role calls for at least 3 years of experience in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or similar scientific work. You should have a solid command of classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling.

Experience with Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, or numerical methods is an advantage. Prior exposure to leading or supporting remote teams of educators, reviewers, researchers, annotators, science writers, or QAs is strongly preferred. Familiarity with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is also a plus.

You should be comfortable working with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management tools. Strong organization is important, especially for maintaining documentation, onboarding material, FAQs, and review systems.

Selection process

The hiring process includes an AI interview, a domain-specific task, and a final interview with a recruiter.

Work arrangement

This is a remote contractor role paid on an hourly basis. There is no immediate project attached to the position at this time, but qualified candidates may be considered for upcoming projects as they arise.

Additional information

Success in this role depends on the ability to judge physics content with precision, communicate feedback clearly, and maintain high standards across distributed contributors. The position is centered on improving the reliability, clarity, and scientific integrity of physics training data for AI systems.