SME CareersDomain experts
Physics Quality Assurance Lead (QAL)
up to $75/hr
About this role, in SME Careers' words
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.
What you'll do
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.
Show all 9 itemsHide the other 6 in Responsibilities
- 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.
Candidate background
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.
- Native fluency in Punjabi
Quoted from selected sections of SME Careers' posting. The full posting may state other conditions. Read the full posting
A closer look
More context, when you need it.
Full job descriptionThe original posting, in SME Careers' words
Posted by SME Careers, reproduced here so you can judge the role before clicking. Original posting ↗
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.
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.
• Native fluency in Punjabi
Location: Remote
How does the pay compare?Across Domain experts roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from SME Careers's posting. How we count.
Pay across the field: Domain experts
Across 698 roles in this field with a stated hourly rate in US dollars, on 18 platforms, the middle half pays $65 to $130 an hour at the top of the range, and the median is $100. This role's top rate, $75, is in the middle half. Of the 260 distinct rates we count, 88 are lower and 165 are higher.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| Alignerr | 149 | $24 to $200 · median $80 |
| micro1 | 136 | $28 to $400 · median $110 |
| AfterQuery | 88 | $45 to $175 · median $100 |
| Handshake AI | 79 | $30 to $300 · median $85 |
| Mercor | 78 | $55 to $450 · median $120 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 6 Oct 2026, and still on SME Careers's list when we last read it on 13 Oct 2026.
- The listed pay has not changed since then.
- Posted by SME Careers
- Not stated
- First seen by AIPayList
- Last seen live
- Listing ID
- 9053b70b-6425-43fd-af8a-8348354f90a8
Pay as the source states it: “Up to $75/hr”.
Platforms sometimes change a rate after posting. Every listing here is re-read at least once a day and corrected when its source moves, so a difference you see on the platform is usually a change made since the last read. How freshness works.
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