You will support AI evaluation research by reviewing content through the standards and technical language of your academic discipline. Your work may include written, visual, diagrammatic, and other subject-specific material.
You will make clear judgments about accuracy, depth, and quality, while identifying errors, unclear points, or gaps that a general reviewer may miss. Each project includes defined annotation and evaluation tasks to complete within its project window.
- Evaluate written content using the standards and technical vocabulary of your field.
- Write structured explanations that support your evaluation decisions.
- Review relevant visual and diagrammatic material, such as schematics, spectra, maps, clinical images, artworks, or data charts.
- Complete annotation and evaluation tasks within each project window.
What it pays and takes
This is remote, asynchronous, project-based contractor work. You can choose which projects to accept, and the expected commitment is up to 10 hours per week for each project window.
- $100 per hour.
- Remote and asynchronous, with up to 10 hours per week for each project window.
- Open to candidates in the project’s listed countries across Africa, Asia, Europe, North America, South America, and Oceania.
- Strong written English and the ability to explain nuanced, subject-specific judgments clearly.
- Current enrollment in a Master's, PhD, MD, or MD/PhD program.
- Deep familiarity with the visual conventions and technical vocabulary of your academic discipline.
- Reliable internet access and the ability to work remotely.
- Helpful background includes visual or image-based research, annotation, data labeling, AI evaluation, multimodal systems, or quantitative methods.
- Experience with microscopy, imaging, diagrammatic modeling, CAD, GIS, spectroscopy, archival visual materials, large language models, or multimodal AI systems may be useful.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training is the human work behind modern AI systems. People review examples, write and rate responses, assess accuracy, and label information so models can learn how to produce more useful results.
Experienced contributors are paid for applying subject knowledge that helps identify subtle errors and judge quality more accurately than a general reviewer.