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Astronomer Quality Assurance Lead (QAL)

SME Careers Remote

Education

Any

Type

Pay Rate

$110/task

Listed

1d ago

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AstroandSpaceQAL-26-135

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About this Role

In this hourly, remote contractor role, you will work as an Astronomy / Astrophysics Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across astronomy and astrophysics AI training projects. You will review AI-generated astronomy/astrophysics content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.

You will assess work for scientific accuracy, physical reasoning, mathematical correctness, terminology quality, unit handling, observational context, clarity, 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 requires strong astronomy/astrophysics expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert teams.

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 astronomy/astrophysics quality leadership will directly help improve the world’s premier AI models by ensuring that astronomy and astrophysics 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.

### Key Responsibilities
- Quality monitoring: Spot-check astronomy/astrophysics items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Scientific review: Evaluate AI-generated astronomy/astrophysics explanations, calculations, diagrams, observational interpretations, comparisons, and step-by-step reasoning for accuracy and clarity.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and astronomy/astrophysics-specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around physical assumptions, units, astronomical terminology, observational methods, formulas, and rubric interpretation.
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
- Documentation: Create and maintain astronomy/astrophysics project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and astronomy/astrophysics-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply astronomy/astrophysics review guidelines consistently and understand updates as projects evolve.
- Risk review: Flag misleading, overconfident, physically impossible, numerically incorrect, or poorly sourced astronomy/astrophysics claims.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for astronomy/astrophysics AI training projects.

### Your Profile
- Bachelor’s, Master’s, or PhD degree in Astronomy, Astrophysics, Physics, Space Science, Planetary Science, Cosmology, or a closely related field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
- 3+ years of experience in astronomy/astrophysics research, teaching, science communication, academic review, data analysis, observatory work, or related scientific workflows.
- Strong understanding of celestial mechanics, stellar evolution, galaxies, cosmology, electromagnetic radiation, observational methods, spectroscopy, planetary systems, black holes, and scientific uncertainty.
- Ability to evaluate astronomy/astrophysics content against detailed rubrics and identify issues such as incorrect physical assumptions, wrong units, flawed calculations, hallucinated facts, misleading explanations, or oversimplified conclusions.
- Familiarity with tools or methods such as Python, astronomical datasets, telescope/observatory data, spectroscopy, photometry, simulations, LaTeX, Jupyter notebooks, or scientific visualization is preferred.
- Experience leading or supporting remote teams of researchers, educators, reviewers, annotators, science writers, or QAs is strongly preferred.
- Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, calibration tasks, and documentation.
- Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus.

🌍 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.

Requirements

  • Advanced degree or strong hands-on professional experience in the domain
  • Ability to pass domain-specific qualification assessments
  • Eligible to create a verified account on Deel (for payments/compliance)
  • Proficiency in English (for instructions and feedback)
  • Available to applicants in 40+ countries

Compensation Analysis

Don't just label data—build a career. SME Careers is unique in the AI training space because it offers a transparent growth ladder. High performers aren't just kept in the queue; they are promoted to Quality Analysts and Project Managers. With weekly transparent payments via Deel and the freedom to work on your own schedule, this is built for modern experts who want long-term engagement.

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Frequently Asked Questions

Is there room for advancement?

Yes. This is a key feature of the platform. They explicitly list a career path: Data Trainer -> Quality Analyst -> QA Lead -> Project Manager. Consistent high-quality work can lead to leadership roles.

How do payments work?

Payments are processed weekly through Deel. This ensures tax compliance and allows them to hire freelancers from over 40 countries securely.

Is the work ongoing or project-based?

Work is project-based, which means assignments can vary in duration and availability. However, top performers often get priority access to new projects.

Is this traditional consulting?

Not exactly. You act as a "Teacher" for advanced AI. Instead of client deliverables, you are given complex scenarios to evaluate. You grade the AI's logic, correct its hallucinations, and provide expert-level reasoning. Your job is to train the model to think like you do.

Why is the pay so high?

This role requires deep, verified expertise. General knowledge isn't enough; the model is specifically being trained on "edge cases"—the rare, difficult, or highly technical nuances that only a senior professional would know.

What is the workload like?

This is cognitive, deep work. Unlike simple data labeling, you might spend 45-60 minutes on a single task, researching citations or verifying complex calculations. Quality is prioritized over speed.

Who is behind SME Careers?

SME Careers is the expert talent division of SuperAnnotate, a leading AI data infrastructure platform. They connect domain experts with high-level AI training projects.