The work
You will create and evaluate AI-training tasks based on professional 3D Slicer and medical imaging workflows. You will also review AI-generated answers and give clear feedback that helps improve model performance.
- Design challenging tasks based on real medical imaging workflows.
- Load and navigate DICOM data and work with anatomical structures.
- Segment images, register image volumes, and build 3D models from scans.
- Measure and quantify regions of interest.
- Use Python scripts and relevant extensions within 3D Slicer.
- Review AI-generated responses for accuracy and quality.
- Explain your decisions and provide structured written feedback.
What it pays and takes
This is an ongoing, project-based opportunity that can be completed remotely alongside other work. Tasks are completed at your own pace, with no set schedule or minimum hour requirement.
- Pay: up to $125 per hour.
- Workload: 20+ hours per week.
- Location: open to candidates in the United States.
- Work arrangement: remote, part-time, and contractor.
- At least two years of work experience using 3D Slicer.
- A completed bachelor's degree or higher in biomedical engineering, medical imaging, radiologic technology, neuroscience, computer science, or a closely related field.
- Ability to explain complex medical imaging concepts clearly.
- Strong written communication and attention to detail.
- Ability to work independently in a remote, asynchronous setting.
- US work authorization.
- Helpful experience includes ITK-SNAP, OsiriX or Horos, MITK, VTK, SimpleITK, MONAI, pydicom, DICOM, or NIfTI.
- Experience in a hospital, research laboratory, medical device company, or imaging core facility is helpful.
- Published research or medical imaging project experience is relevant.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is a free platform where people find AI training and data-labeling work, build a profile, and apply to projects.
About AI training work
AI training is the human work behind artificial intelligence, including preparing examples and reviewing model responses. Experienced contributors are paid for applying specialist knowledge, following detailed guidelines, and judging whether AI outputs are accurate and useful.