Physics Quality Assurance Lead (QAL)
SME Careers • Remote
Education
PhD
Type
Hourly
Pay Rate
$75/hr
Listed
53d ago
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- Interview
- Required i As stated in the listing: “Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.”
About this role
From the SME Careers listing
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.
Key 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.
Your profile
- 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
🌍 Geographic Availability This role is available to qualified experts globally. However, as a U.S.-based company, SME Careers complies with U.S. export control and sanctions regulations. Eligibility depends on your country of residence and any applicable trade restrictions. Some projects may have additional language or region-specific requirements.
💰 Pay Rate May Vary Based on Your Location Your actual compensation will be determined based on your location and local market conditions. See regional job listings for localized pay information, or the application process will clarify your specific rate.
If you're unsure whether you're eligible, proceed with the application—SME's screening will determine your jurisdiction status and applicable compensation.
How long hiring takes
Across the AI training platforms we refer candidates to, the median gap between referral and hire is about 30 days. It varies by platform and role, so treat it as a rough guide for this one.
Why this role
This Physics QAL role pays $75/hr to oversee quality and trainer performance across physics AI training projects, reviewing other trainers' work rather than reviewing model responses directly. SME Careers wants a Bachelor's, Master's, or PhD in Physics or a closely related field, plus three or more years of research, teaching, or lab experience. That combination of an advanced physics background and review leadership is what separates this role from a standard physics content review position.
Talent pool
We're light on STEM candidates
We've matched 65 people with a STEM background against 726 STEM listings we've tracked, so most go out without one. Set up a profile and we'll consider you for a role like this one.
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Common questions
Is SME Careers work ongoing or project-based?
Project-based. Assignment duration and availability vary by project, but consistently high-quality work earns priority access to whatever comes next.
Does SME Careers offer a path for advancement?
Yes. SME Careers is unusual among AI training platforms for this: it publishes an explicit career ladder, Data Trainer, then Quality Analyst, then QA Lead, then Project Manager. Consistent, high-quality work is what moves you up it.
Is academic-niche AI training just data labeling?
No, it's closer to academic research. Expect to write or verify complex proofs, solve advanced equations, or check the logic behind a model's step-by-step reasoning. The goal is teaching AI systems to reason deeply within your specific field.
Do I need a PhD for academic-niche AI training roles?
For the top pay tiers, a PhD or current enrollment is usually expected. But the domain assessment is what decides it: if you can solve the problems, the degree becomes secondary.
What does STEM work look like for a Physics Quality Assurance Lead (QAL)?
Tasks here are scoped to STEM, not generic labeling. As a Physics Quality Assurance Lead (QAL), expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to STEM) rather than following a one-size-fits-all rubric. If you don't have hands-on STEM background, this is likely not the right listing to start with.
What specific skills does this listing call for?
thermodynamics, Mechanics, Physics QA, and Scientific review are named directly in the listing. If you don't have hands-on experience with these, expect the screening process to test for them directly rather than accepting adjacent experience as a substitute.
How much does this specific role pay?
This listing is posted at $75/hr, a per-task rate. Pay can change between when we last checked the listing and when you apply, so confirm the current number on the platform's own application page before committing time.
What happens when I click Apply on this listing?
You'll be taken to SME Careers's external site to complete your application there. This listing links through a referral, but the process is identical to applying directly; the link just routes you correctly. Create an account on their site and follow their onboarding steps.
Is a PhD required?
For this specific role, yes, or near-equivalent professional depth. The credential gate is enforced at the assessment stage, not just on paper. That said, active PhD candidates and people with equivalent published research have qualified without a formal degree. The assessment is the real filter.
Is there an interview?
Yes. Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Who is behind SME Careers?
SME Careers is the expert talent division of SuperAnnotate, an AI data infrastructure platform, connecting domain experts with high-level AI training projects.