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.
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.
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.
Skills
- Astrophysics
- Scientific Reasoning
- LLM Evaluation
- Stellar Evolution
- Observational Astronomy
- Astronomy
- Physics Review
- Cosmology
- AI Training
- Trainer Feedback
Additional skills
- Astronomy
- Astrophysics
- Cosmology
- Celestial Mechanics
- Stellar Evolution
- Galaxy Formation
- Black Holes
- Planetary Science
- Electromagnetic Radiation
- Observational Astronomy
- Spectroscopy
- Photometry
- Telescope Data Analysis
- Astronomical Data Analysis
- Scientific Uncertainty Analysis
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- LaTeX
- Scientific Visualization
- Simulations
- Peer Review
- Rubric-Based Evaluation
- Quality Assurance (QA)
- Scientific Writing
- Data Annotation
- LLM Evaluation
- Remote Team Leadership
