Remote Part-Time Astronomy AI Evaluator

OpenTrain AI; Northern (KY); Hybrid; USD 55,000 - 138,000; Part time

Benefits offered by this job

Remote contract engagement

Part-time

Remote collaboration

Contractor engagement

Job summary

OpenTrain AI is seeking an experienced astronomy or space science specialist to evaluate AI-generated content across a broad range of scientific topics. You will assess scientific accuracy, logical soundness, completeness, clarity, units, assumptions, approximations, uncertainty, and limits of inference.

The work is designed for contributors who can apply strong quantitative reasoning and provide high-quality scientific judgment on advanced model outputs.

Qualifications

• This role requires a substantial professional background in astronomy or a closely related space science field, with ability to evaluate quantitative work carefully.
• A bachelor's degree or higher is required; advanced study strongly preferred.

Responsibilities

• Evaluate AI-generated scientific content for accuracy and logical soundness.
• Review prompts, model answers, and step-by-step solutions.
• Check units, assumptions, uncertainty, and limitations.
• Provide structured scientific feedback on model outputs.
• Assess topics in orbital mechanics, astrophysics, cosmology, planetary science, space physics, and data interpretation.

Skills

Astronomy experience

Quantitative reasoning

Scientific evaluation

Orbital mechanics

Uncertainty analysis

Communication of limits

Python familiarity

Education

Bachelor's degree in physics/astronomy/space science/engineering

Master's degree or PhD preferred

Tools

Python

NumPy

SciPy

Data pipelines

Photometry

Spectroscopy

Mission datasets

Job description

OpenTrain AI is seeking an experienced astronomy or space science specialist to evaluate AI-generated content across a broad range of scientific topics. You will assess scientific accuracy, logical soundness, completeness, clarity, units, assumptions, approximations, uncertainty, and limits of inference.

The work is designed for contributors who can apply strong quantitative reasoning and provide high-quality scientific judgment on advanced model outputs.