{"html":"<p>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.</p>\n<p>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.</p>\n<p>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.</p>\n<p>Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.</p>\n<h3>Responsibilities</h3><ul><li>Spot-check physics items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.</li><li>Review AI-generated physics explanations, calculations, diagrams, derivations, experimental interpretations, and step-by-step reasoning.</li><li>Update trainers/QAs on Discord about guidelines, workflow updates, and physics-specific quality expectations.</li><li>Respond to questions around physical assumptions, formulas, units, derivations, diagrams, experimental setups, and rubric interpretation.</li><li>DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.</li><li>Create and maintain physics documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.</li><li>Run onboarding/training calls for physics contributors.</li><li>Flag misleading, numerically incorrect, physically impossible, unsafe, or poorly contextualized physics claims.</li><li>Identify recurring quality gaps and improve physics QA workflows.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s, Master’s, or PhD degree in Physics, Applied Physics, Engineering Physics, Astrophysics, Mathematics, Engineering, or a closely related quantitative/scientific field.</li><li>Strong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.</li><li>3+ years of experience in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or related scientific workflows.</li><li>Strong understanding of classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling.</li><li>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.</li><li>Familiarity with tools or methods such as Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, and numerical methods is preferred.</li><li>Experience leading or supporting remote teams of educators, reviewers, researchers, annotators, science writers, or QAs is strongly preferred.</li><li>Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.</li><li>Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.</li><li>Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus.</li><li>Native fluency in Punjabi</li></ul>"}