Astronomer QA Lead - Remote
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Key skills for this role
About the Role
YO IT Consulting is looking for an Astronomer Quality Assurance Lead to oversee quality and trainer performance across astronomy/astrophysics AI training projects. In this remote contract role, you will review AI-generated astronomy content, provide feedback, and ensure scientific accuracy.
Key Skills for This Role
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
Requirements
- 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
Full Job Posting
About This Role
- In this hourly, remote contractor role, you will work as an Astronomer Quality Assurance Lead 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.
- 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.
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.
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.
Selection Process
- 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.
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