Astronomy QA Lead for Astrophysics AI Training
Overview
In this remote, hourly contractor role, you will oversee quality for astronomy and astrophysics AI training projects. Review AI-generated explanations, calculations, and observational interpretations for accuracy and clarity against detailed rubrics. Coordinate with remote contributors via Discord, Google Sheets, and trackers to maintain consistent quality standards. You will help build documentation and onboarding materials to scale QA workflows across astronomy pipelines.
What You'll Do9
- 1Spot-check astronomy and astrophysics items, flag quality issues, and provide targeted feedback via direct messages while escalating recurring problems.
- 2Evaluate AI-generated explanations, calculations, diagrams, and observational interpretations for accuracy and clarity against project rubrics.
- 3Communicate project updates, guidelines, and quality expectations to trainers and QAs on Discord, ensuring alignment with review standards.
- 4Answer trainer/QA questions about physical assumptions, units, astronomical terminology, observational methods, formulas, and rubric interpretation with clear, actionable guidance.
- 5Manage contributor activity by nudging inactive teammates, tracking follow-ups, and flagging availability issues when needed.
- 6Create and maintain project documentation including style guides, trackers, FAQs, quality notes, calibration tasks, and onboarding materials.
- 7Lead onboarding and training sessions to explain project expectations, workflows, rubrics, and astronomy-specific review requirements.
- 8Ensure consistent application of astronomy/astrophysics review guidelines as projects evolve across the team.
- 9Identify quality gaps, propose workflow improvements, and help build scalable QA processes for astronomy AI training projects.
Requirements10
- 1Bachelor’s, Master’s, or PhD in astronomy, astrophysics, physics, space science, planetary science, cosmology, or closely related field
- 2Strong English language skills for following guidelines and delivering clear written feedback
- 33+ years in astronomy/astrophysics research, teaching, science communication, academic review, data analysis, observatory work, or related scientific workflows
- 4Deep understanding of celestial mechanics, stellar evolution, galaxies, cosmology, electromagnetic radiation, observational methods, spectroscopy, planetary systems, black holes, and scientific uncertainty
- 5Ability to evaluate astronomy/astrophysics content against detailed rubrics and identify incorrect physical assumptions, wrong units, flawed calculations, or misleading conclusions
- 6Familiarity with tools or methods such as Python, astronomical datasets, telescope/observatory data, spectroscopy, photometry, simulations, LaTeX, Jupyter notebooks, or scientific visualization is preferred
- 7Experience leading or supporting remote teams of researchers, educators, reviewers, annotators, or QAs is strongly preferred
- 8Comfortable working in fast-moving remote environments using Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems
- 9Highly detail-oriented and organized, capable of maintaining style guides, FAQs, trackers, onboarding materials, calibration tasks, and documentation
- 10Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus
Who Should Apply
Ideal candidates bring advanced astronomy or astrophysics QA experience and a proven ability to align AI training outputs with rubrics and client expectations. This role suits researchers, educators, or reviewers who thrive in remote collaboration and can articulate complex physics clearly in written feedback. It’s a less good fit for individuals lacking remote team experience or with limited English proficiency or attention to detail. If you have limited exposure to AI training workflows or rubric-based evaluation, you may score lower on fit. Strong candidates demonstrate a history of maintaining documentation, onboarding teammates, and driving quality improvements in distributed teams.
Salary Insight
Hourly rate: $110/hr. This compensation reflects senior, remote contractor expertise in astronomy AI QA. Typical for contract-based expert roles within AI data services networks.
Location
Required Skills
Application Tip
Highlight a concrete instance where you used a rubric to correct QA findings in astronomy data, including the number of items adjusted and the impact on model outputs; mention tools used (e.g., Discord for communication, Google Sheets for tracking).
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How your application is processed
1Application received
Your resume and details are logged the moment you apply.
2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
3Employer sees qualified profiles only
Only candidates who clear screening move forward.
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