Astronomer QA Lead

Role overview

This remote contractor position is for an Astronomy Quality Assurance Lead who will help maintain the accuracy and consistency of astronomy and astrophysics AI training work. The focus is on reviewing AI-produced content and the work of trainers and QAs, then giving clear written feedback so project standards are met.

The role sits within a fast-scaling AI data services environment that supports major AI companies and foundation model labs. By checking that astronomy and astrophysics training data is scientifically sound, clearly written, and aligned with client needs, you will directly contribute to improving leading AI models.

There is no active project to join right away. If you are a good fit, you may be contacted first when relevant work becomes available and may also gain access to future opportunities through the expert network.

What the role involves

You will assess content for scientific accuracy, physical logic, calculation correctness, terminology, unit usage, observational context, clarity, formatting, instruction compliance, and adherence to project rubrics. The role also includes spotting repeated quality problems, sharing updates with trainers and QAs, helping with onboarding, maintaining project documentation, and supporting contributor reactivation when work becomes inconsistent.

Key responsibilities

• Review astronomy and astrophysics items regularly, identify quality concerns, and share actionable feedback with contributors.
• Check AI-generated explanations, calculations, diagrams, observational interpretations, comparisons, and step-by-step reasoning for correctness and clarity.
• Communicate project updates, workflow changes, item guidelines, and quality standards to trainers and QAs through remote channels.
• Answer questions from trainers and QAs with clear guidance on assumptions, units, terminology, formulas, observational methods, and rubric interpretation.
• Reach out to inactive contributors, encourage re-engagement, track follow-ups, and highlight availability concerns where needed.
• Build and update project materials such as style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding documents.
• Plan and run onboarding or training sessions to explain project expectations, review workflows, and domain-specific quality requirements.
• Keep review standards aligned across trainers and QAs as project rules evolve.
• Escalate risky content such as physically impossible statements, misleading claims, weak sourcing, or incorrect numerical reasoning.
• Recommend workflow improvements and help shape scalable QA processes for astronomy and astrophysics data work.

Requirements

• A bachelor’s, master’s, or PhD degree in Astronomy, Astrophysics, Physics, Space Science, Planetary Science, Cosmology, or a closely related subject.
• At least 3 years of experience in astronomy or astrophysics research, teaching, science communication, academic review, data analysis, observatory work, or a similar scientific setting.
• Strong command of English for reading guidelines, coordinating with teams, and writing precise feedback.
• Solid knowledge of celestial mechanics, stellar evolution, galaxies, cosmology, electromagnetic radiation, observational methods, spectroscopy, planetary systems, black holes, and scientific uncertainty.
• Ability to judge domain content against detailed rubrics and detect incorrect assumptions, unit mistakes, flawed calculations, hallucinations, misleading explanations, or oversimplified conclusions.
• Preferred familiarity with Python, astronomical datasets, observatory or telescope data, spectroscopy, photometry, simulations, LaTeX, Jupyter notebooks, or scientific visualization.
• Experience leading or supporting remote teams of researchers, educators, reviewers, annotators, science writers, or QAs is strongly preferred.
• Comfort working in fast-paced remote setups with tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Strong organization, attention to detail, and the ability to manage style guides, FAQs, trackers, onboarding assets, calibration work, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a major advantage.

Additional information

This is an hourly remote contractor role. The selection process includes an AI interview, a domain-specific task, and an interview with a recruiter. The position is meant to support future projects rather than an immediate assignment.

Work context

You will collaborate with remote expert teams and help ensure that contributors understand quality expectations and apply them consistently. The work requires structured communication, strong judgment, and the ability to manage quality workflows in a distributed environment.

Terms and conditions

No immediate project is available for this role at present. Qualified candidates may be contacted when relevant work opens up, and successful applicants may be considered for future opportunities within the expert network.