Physics Quality Assurance Lead (QAL)

In this hourly, remote contractor role, you will work as a 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.

You will assess work for scientific accuracy, physical reasoning, calculation correctness, unit consistency, formula use, conceptual clarity, experimental understanding, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your physics quality leadership will directly help improve the world’s premier AI models by ensuring that physics training data is accurate, physically sound, clearly explained, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

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.
• DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Create and maintain physics documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.
• Run onboarding/training calls for physics contributors.
• Flag misleading, numerically incorrect, physically impossible, unsafe, or poorly contextualized physics claims.
• Identify recurring quality gaps and improve physics QA workflows.

Requirements

• 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.
• Experience leading or supporting remote teams of educators, reviewers, researchers, annotators, science writers, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus.

Eligible countries: The United States