The work
You will evaluate AI-generated responses, prompts, and step-by-step solutions across astronomy and space science. Your feedback will help identify scientific errors and improve how AI explains technical ideas, uncertainty, and limits.
- Check scientific accuracy, logical soundness, completeness, and clarity.
- Review units, assumptions, approximations, uncertainty, and limits of inference.
- Find conceptual errors, bad assumptions, missing constraints, formula mistakes, and overconfident claims.
- Assess work involving orbital mechanics, stellar and galactic astrophysics, cosmology, planetary science, space physics, and data interpretation.
- Challenge models with realistic scenarios, calculations, and research-style reasoning tasks.
- Review and refine prompts, model answers, and step-by-step solutions.
- Provide clear, structured feedback on technical accuracy and uncertainty.
What it pays and takes
This is a remote, part-time contract engagement for contributors with substantial astronomy or space science experience. The role is listed at hourly rates from $40 to $100, with the advertised rate depending on the contributor's country.
- Pay: $40-$100 USD per hour, depending on country.
- Time: 20+ hours per week.
- Experience: 4+ years of professional experience in astronomy or a closely related space science field.
- Education: A bachelor's degree in physics, astronomy, space science, engineering, or a related field; a master's degree or PhD is strongly preferred.
- Language: English.
- Location: Remote collaboration for qualified contributors in Bangladesh, Bhutan, Brazil, Cambodia, Germany, India, Indonesia, Malaysia, Nepal, Pakistan, Singapore, Sri Lanka, Thailand, the Philippines, the United States, Timor-Leste, or Vietnam.
- Knowledge: Orbital mechanics, radiative processes, stellar and galactic physics, coordinate systems, time standards, statistics, and scientific modeling assumptions.
- Technical judgment: Use orders of magnitude, unit consistency, limiting cases, and error propagation to validate results.
- Communication: Explain limitations responsibly and challenge overconfident scientific claims.
- Helpful experience: AI training, annotation, red-teaming, technical content evaluation, Python, NumPy, SciPy, data pipelines, catalog queries, photometry, spectroscopy, mission datasets, orbit determination, or related quantitative analysis.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training work is the human review behind systems that learn to answer questions and solve problems. Specialists are paid for checking model outputs because accurate feedback helps AI handle technical subjects more reliably and communicate uncertainty honestly.