Remote AI Radiology Trainer for Medical Imaging

DataAnnotation; Katy (TX); Remote; USD 110,000 - 172,000; Full time

Benefits offered by this job

Flexible work schedule

Remote project-based work

Job summary

DataAnnotation is seeking an experienced radiologist or credentialed imaging technologist to help train AI models by interpreting diagnostic images such as chest X-rays, ultrasounds, and nuclear medicine scans, and by documenting reasoning and impressions.

You will review de-identified studies in a clinical context, write comprehensive structured reports, provide clear impressions and recommended next steps, and log discrepancies between interpretations to improve model outputs.

Qualifications

• Fluency in English; residency-trained or board-certified radiologist, or credentialed/licensed imaging technologist.
• Hands-on experience performing or interpreting radiology studies.
• Clear, well-organized writing, with ability to justify conclusions.
• Detail-oriented with precise documentation.

Responsibilities

• Review de-identified studies (chest X-rays, ultrasounds, nuclear medicine scans) with clinical context.
• Write comprehensive, structured reports documenting findings, including subtle and incidental ones.
• Provide clear impressions and recommended next steps (e.g., follow-up imaging).
• Compare two independent interpretations, resolve differences, and log discrepancies.

Skills

English fluency

Medical image interpretation

Structured reporting

Clinical correlation

Detail-oriented

Education

Residency-trained radiologist or board-certified radiologist

Credentialed imaging technologist

Job description

DataAnnotation is seeking an experienced radiologist or credentialed imaging technologist to help train AI models by interpreting diagnostic images such as chest X-rays, ultrasounds, and nuclear medicine scans, and by documenting reasoning and impressions.

You will review de-identified studies in a clinical context, write comprehensive structured reports, provide clear impressions and recommended next steps, and log discrepancies between interpretations to improve model outputs.