Remote Ultrasound AI Trainer for Radiology Reads

DataAnnotation; Chicago (IL); Remote; USD 103,000 - 172,000; Full time

Job summary

DataAnnotation is seeking an experienced Radiologist or imaging professional to help train AI models by reviewing studies and drafting structured reports. You will provide impressions, recommend next steps, and log discrepancies between interpretations to improve model accuracy.

Responsibilities include comparing independent reads, detailing findings, and identifying gaps in model-generated reports. This is remote, project-based work with opportunities across multiple imaging modalities.

Qualifications

• Fluency in English (native or bilingual).
• Residency-trained or board-certified radiologist, or credentialed/licensed imaging technologist.
• Hands-on experience performing or interpreting studies.
• Clear, well-organized writing, and the ability to say why a report is wrong, not only that it is.
• Detail-oriented.
• Only applicants based in Australia, Canada, Ireland, New Zealand, the United Kingdom, or the United States will be considered for this role.

Responsibilities

• Review de-identified studies (chest X-rays, ultrasounds, nuclear medicine scans) alongside their clinical context.
• Write comprehensive, structured reports that document every finding, including subtle and incidental ones.
• Give clear impressions and recommend next steps, such as follow-up scans or further workup.
• Compare two independent interpretations of the same study, resolve the differences, and log each discrepancy.
• Pinpoint what model-generated reports miss, misstate, or leave incomplete.

Skills

Fluency in English

Residency-trained radiologist

Board-certified radiologist

Imaging technologist background

Hands-on interpretation

Clear report writing

Education

Radiology residency

Board certification in radiology

Job description

DataAnnotation is seeking an experienced Radiologist or imaging professional to help train AI models by reviewing studies and drafting structured reports. You will provide impressions, recommend next steps, and log discrepancies between interpretations to improve model accuracy.

Responsibilities include comparing independent reads, detailing findings, and identifying gaps in model-generated reports. This is remote, project-based work with opportunities across multiple imaging modalities.