Lead Sonographer - AI Trainer

DataAnnotation

San Mateo (CA)

Remote

USD 83,000 - 172,000

Full time

10 days ago

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Benefits offered by this job

Flexible schedule
Remote work
Project-based

Job summary

DataAnnotation is seeking an experienced radiologist or imaging technologist to train AI models by reading diagnostic images and crafting structured reports. You will compare interpretations, explain why a report may be wrong, and help guide next steps in imaging workups.

Excellent writing and clinical reasoning are essential. This remote, project-based role supports radiology reads across chest X-ray, ultrasound, nuclear medicine, and cross-sectional imaging such as CT/MRI.

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 with rationale for findings.
  • Detail-oriented and precise in documentation.

Responsibilities

  • Review de-identified studies (chest X-rays, ultrasounds, nuclear medicine scans) with clinical context.
  • Write comprehensive, structured reports documenting findings, including subtle/incidental ones.
  • Provide clear impressions and recommend follow-up steps or additional workup.
  • Compare two independent interpretations, resolve differences, log discrepancies.
  • Identify model-report gaps and explain missing or inaccurate items.

Skills

Fluent English
Hands-on experience
Clear writing ability

Education

Residency-trained radiologist
Board-certified radiologist
Credentialed imaging technologist

Job description

We’re looking for an experienced Radiologist to help train AI models. You’ll measure how well frontier models interpret medical images, check their reasoning, and help raise the quality of each new version.

This is a new and growing area: teaching AI to read diagnostic images, such as chest X-rays, ultrasounds, and nuclear medicine scans, the way a skilled radiologist would. The role is open to both radiologists and imaging technologists. Your reads, impressions, and written rationale become the signal that shapes the next generation of imaging AI.

What you’ll actually do
  • 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.

Problems draw on whatever you know best:

  • Radiography: chest and musculoskeletal plain film.
  • Ultrasound and sonography.
  • Nuclear medicine and molecular imaging.
  • Cross-sectional imaging: CT and MRI.
  • Subspecialty reads: neuro, body, cardiothoracic, and emergency.
Roles this fits

Common backgrounds: Radiologist, Ultrasound Technologist, Nuclear Medicine Technologist.

What we look for
  • Fluency in English (native or bilingual level).
  • A residency-trained or board-certified radiologist, or a credentialed or licensed imaging technologist (radiologic, ultrasound, or nuclear medicine).
  • 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.
Compensation

Up to $125+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income.

About DataAnnotation

DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.

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