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Lung Sound and Respiratory Annotation

Remote · Global Hourly Rolling start $50–$100/hr
Overview

Annotate lung sound recordings and respiratory findings, and review AI model outputs, to help train and validate clinical biosignal algorithms for respiratory disease.

About this job

We're seeking board-certified pulmonologists to annotate lung sound recordings and review AI-generated model outputs against expert judgment. This work trains and validates AI algorithms for lung sound analysis used in real clinical AI products. You will mark breath sound phase, quality, and adventitious findings using a provided rubric.

What you'll do
  • Listen to short lung sound recordings in the web workspace, with location/lung field noted for each.
  • Mark inspiration and expiration phases and rate overall breath sound quality (normal, diminished, absent).
  • Identify and annotate adventitious sounds - wheezes, crackles/rales, rhonchi, stridor, pleural rub by timing, location, and quality.
  • Label signal quality and overall normal versus abnormal classification.
  • Review AI model outputs and mark where they are correct or incorrect.
  • Apply the project-specific rubric and instructions consistently across recordings.
Required skills
Respiratory / Pulmonology
Who can apply
  • Field of study: Pulmonology / Respiratory Medicine
About grayder

Life science AI is only as good as the experts who train it.

A vetted network of doctors, scientists, and regulatory specialists who train and evaluate the models behind drug discovery, clinical trials, medical devices, and healthcare. Remote, flexible, matched to your field.

What specialists do

  • Annotation — labelling clinical text, imaging and molecular data
  • Model evaluation — scoring outputs against expert standards
  • Rubric design and execution
  • Fine-tuning data — writing expert responses
  • Q&A sets, summaries and reports

Why specialists join

  • Work matched to your speciality
  • Remote and flexible — on your own schedule
  • Credentialed by your peers
  • Meaningful impact on healthcare AI
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