Remote radiology jobs
Apply radiology subject-matter expertise to the human side of building medical AI. Radiology annotation work means labeling images and reports, checking model outputs, and following clinical annotation protocols so models learn to recognize anatomy, findings, and measurements. OpenTrain lists projects where clinicians, technologists, and trained annotators can build a profile, complete onboarding checks, and apply to remote, flexible annotation and QA tasks. Creating an OpenTrain account is free and lets you find projects that match your skills and availability.
3 open positions
CT scan - bone segmentation of lower limb - on demand
Perform on-demand segmentation of lower-limb CT (DICOM) to create high-quality bone masks and 3D anatomical models with a 24-hour turnaround. Contractor role for experienced CT bone segmenters located in CO, MX, IN, CR, or AR, paid hourly.
View jobPosted Jun 3, 2026
US Board-Certified Radiologists MD Needed for CT/MRI Annotation
Join OpenTrain AI to label CT and MRI DICOM studies as a US-based, board-certified radiologist (MD). Contract work paying $150/hour performing polygon and segmentation masks and diagnostic labeling for medical imaging projects.
View jobPosted Jan 15, 2026
Board-Certified Radiologist – Medical Image Annotation
US-based, board-certified radiologists are needed for a 1–2 month remote project annotating DICOM images with bounding boxes and classifications; flexible 5–15 hrs/week schedule and $150/hr on a secure platform.
View jobPosted Feb 27, 2025
What radiology annotation work involves
Radiology-focused AI training centers on preparing and reviewing imaging data so models can learn clinical patterns. Typical tasks include marking regions of interest, drawing segmentations or bounding boxes around lesions, classifying studies by finding or modality, and matching images to structured labels or report excerpts.
Work can also include labeling temporal series (for CT or MRI), annotating measurements (e.g., lesion size), verifying automated outputs, and performing quality-control passes against project guidelines. Most projects provide detailed annotation protocols and web-based tools; your job is to apply consistent, guideline-driven decisions so model training is reliable.
- Pixel-level segmentation, bounding boxes, and region annotation on x‑ray, CT, MRI, ultrasound, or nuclear imaging.
- Study- and series-level classification: exam type, presence/absence of findings, urgency flags.
- Transcribing or mapping radiology report elements to structured labels for supervised training.
- Quality assurance: reviewing model suggestions, adjudicating disagreements, and following annotation guides.
Skills and experience that help
Domain knowledge of cross-sectional anatomy, common pathologies, and modality-specific appearances is highly valuable. Formal clinical training (radiologists, residents, technologists) is often useful but not always required; careful attention to imaging detail and the ability to follow written protocols matter most.
Familiarity with medical image viewers or basic understanding of DICOM conventions, windowing/level, and multiplanar series is an advantage. Strong observational skills, consistent decision-making, ability to document edge cases, and clear communication with project leads help you move from onboarding to higher-responsibility tasks.
- Clinical background or radiology training speeds onboarding for specialist projects.
- Comfort with viewing tools, scrolling through series, and adjusting window/level.
- Ability to apply written guidelines consistently and flag ambiguous cases for review.
- Time management and reliability for deadlines and QA cycles.
Who tends to do well in these roles
People who succeed combine clinical familiarity with methodical annotation habits. Radiologists and trainees often take on complex adjudication or protocol development roles; radiologic technologists, medical students, and allied health professionals commonly perform image labeling and QA.
This work suits people seeking remote, part-time or project-based work that leverages medical expertise. Projects that require deeper specialty knowledge typically reward that expertise, so highlighting modality experience (e.g., neuro CT, chest x‑ray, musculoskeletal MRI) in your profile helps you qualify for more technical assignments.
- Clinicians or trainees who want flexible, remote work contributing to AI development.
- Technologists and imaging specialists who know modality workflow and image features.
- Detail-oriented people who follow protocols and document uncertain cases clearly.
- Those building experience in medical AI annotation, QA, or protocol review.
How hiring and onboarding work on OpenTrain
Create a free OpenTrain profile and list your radiology-related credentials, modalities, and languages. Many projects require short qualification tests or sample annotations so clients can verify your ability to follow their guidelines.
Once accepted, projects supply task-specific training materials and annotation guidelines. You’ll typically work in a web tool, follow a checklist, and submit work for periodic QA. Work arrangements are remote and project-based: choose assignments that fit your schedule, complete required onboarding, and receive feedback to improve accuracy.
- Sign up, complete your profile, and indicate modalities and specialties.
- Take project qualification tasks or tests to demonstrate guideline adherence.
- Follow onboarding materials and start with supervised or reviewed tasks.
- Do remote, task-based work and receive feedback through QA and review cycles.
Frequently asked questions
- Do I need to be a radiologist to do radiology annotation work?
- Not always. Projects vary: some need radiologists or senior clinicians for adjudication, protocol design, or complex labeling, while others accept trained technologists, residents, medical students, or non-clinical annotators who pass qualification tests. What matters most is the ability to follow project guidelines consistently; list your relevant experience on your OpenTrain profile to match to the right projects.
- Is radiology annotation work remote and flexible?
- Yes—AI training work is commonly remote and project-based. Many radiology annotation tasks can be completed from a computer with internet access and a compatible viewer. Projects differ in scheduling: some let you pick tasks at your convenience, others have deadlines or shift-like batches. Check each project’s requirements before applying.
- Will I be working with identifiable patient data?
- Projects generally handle data privacy through de-identification and platform safeguards, and they provide instructions on permitted data handling. You may be asked to complete confidentiality or data-use training as part of onboarding. If you have concerns about sensitive data, review the project’s privacy notes and requirements before applying.
- How do I demonstrate my radiology skills on OpenTrain?
- Complete your OpenTrain profile with your clinical background, modality experience, and any certifications. Many projects require qualification tasks—sample annotations or tests—so preparing a concise description of your experience and practicing common annotation tasks helps. High-quality, guideline-consistent submissions during qualification improve your chances of selection for specialized projects.
- How is annotation quality reviewed and improved?
- Projects use QA workflows such as consensus reviews, expert adjudication, and automated checks. You’ll receive feedback on disagreements and may redo tasks or review examples in training materials. Consistently following guidelines, asking clarifying questions, and documenting ambiguous cases helps improve accuracy and can lead to more advanced assignments.