Remote Physics QA Lead for AI Training
Pubblicato il 26-10-2026 - YO IT CONSULTING in Roma
pstrongJob Descriptionbr/strong /ppstrongJob Descriptionbr/strongstrongJob Title: /strong Physics Quality Assurance LeadbrstrongJob Type: /strong ContractbrstrongLocation: /strong RemotebrstrongAbout This Rolebr/strongIn this hourly, remote contractor role, you will work as a Physics Quality Assurance Lead 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 a fast-growing AI Data Services company 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.brstrongYour Profilebr/strongulliBachelor’s, Master’s, or PhD degree in Physics, Applied Physics, Engineering Physics, Astrophysics, Mathematics, Engineering, or a closely related quantitative/scientific field. /liliStrong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback. /lili3+ years of experience in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or related scientific workflows. /liliStrong understanding of classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling. /liliAbility 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. /liliFamiliarity with tools or methods such as Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, and numerical methods is preferred.
/liliExperience leading or supporting remote teams of educators, reviewers, researchers, annotators, science writers, or QAs is strongly preferred. /liliComfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems. /liliHighly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation. /liliExperience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus.br/li /ulstrongKey Responsibilitiesbr/strongulliSpot-check physics items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues. /liliReview AI-generated physics explanations, calculations, diagrams, derivations, experimental interpretations, and step-by-step reasoning. /liliUpdate trainers/QAs on Discord about guidelines, workflow updates, and physics-specific quality expectations. /liliRespond to questions around physical assumptions, formulas, units, derivations, diagrams, experimental setups, and rubric interpretation. /liliDM inactive contributors, encourage activation, track follow-ups, and flag availability issues. /liliCreate and maintain physics documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials. /liliRun onboarding/training calls for physics contributors. /liliFlag misleading, numerically incorrect, physically impossible, unsafe, or poorly contextualized physics claims. /liliIdentify recurring quality gaps and improve physics QA workflows /li /ul /p