Radiologist - Chest CT Annotation - AI Project (USA)

We are seeking radiologists with a chest/thoracic background to carry out structural annotation on de-identified chest CT scans, with a focus on spatial accuracy and consistency against detailed annotation guidelines.

You will work asynchronously on a dedicated annotation platform, marking points, boxes and measurements on findings that have already been flagged upstream, to support the development of clinical AI imaging models.

This is a remote, flexible and short-term project.

Key Responsibilities:

• Click-point placement: Mark specific coordinates on a scan, such as the centre of a nodule.
• Bounding boxes: Draw boxes around defined regions of interest, such as a flagged lesion.
• Simple measurements: Record diameter, or distance between two marked points, on segmentations or polygons already drawn.
• Guideline application: Work to an annotation guideline covering placement conventions, boundary definitions and measurement rules, applied consistently across a large volume of studies.
• Edge case handling: Flag ambiguous cases with a short rationale so guidance can be refined.

This is structural markup project with no interpretation required. You will not be reading scans for diagnosis, judging malignancy, or making clinical decisions.

Qualifications:

• Registration: Registered radiologist in your country of practice with active license
• Experience: 3–10 years’ experience in radiology, with a chest/thoracic focus
• Knowledge: Strong grounding in thoracic anatomy and cross-sectional CT.
• Attention to detail: Proven accuracy and consistency in image review or measurement work.
• Technical proficiency: Comfortable using structured annotation tools and digital platforms.
• Prior imaging annotation experience is an advantage but not required.
• Language: Native or near-native written English.

Legal Status:

• You will have the right to work in your country of residence.
• You will work as an independent contractor.

Why Join Us?

• Flexible Work Arrangements: Part-time, remote and fully asynchronous.
• Competitive Compensation: Hourly rate, paid weekly.
• Professional Development: Hands-on experience in clinical AI development and medical imaging, shaping how AI systems localise and measure findings on CT.