Training Turk

Mercor listing

Emergency Medicine Physician (MD)

$150–170/hr

The role in one line

A board-certified emergency physician annotates and evaluates AI-generated clinical documentation to ensure it meets safety and accuracy standards for medical AI systems.

Written by Training Turk from the public listing; it may be incomplete or out of date. Read the full posting on Mercor.

What you would do

  • Review AI-generated ED summaries and source charts for clinical accuracy and completeness
  • Identify and classify errors including missing clinical details, hallucinations, and documentation gaps
  • Provide detailed written feedback and recommendations to product and engineering teams
  • Contribute to developing and refining annotation guidelines based on clinical expertise
  • Evaluate AI performance against standards expected by practicing emergency physicians

Who they are looking for

  • MD with completed Emergency Medicine residency and 2-3+ years of board-certified practice experience
  • US-based with flexibility to work remote and part-time around clinical schedule
  • Proficiency in identifying subtle clinical errors and documenting feedback clearly
  • Experience synthesizing complex patient information into concise clinical summaries
  • Preferred: prior experience with AI annotation, model evaluation, or quality assurance projects

Skills this role asks for

clinical documentation evaluationemergency medicine expertiseai output quality assessmentpatient chart analysismedical ai annotationclinical accuracy validationdocumentation gap identificationed clinical workflowsfeedback structuringguideline developmenthallucination detection

What the interview is likely to probe

  1. 1.AI hallucination recognition

    Emergency medicine decisions depend on accurate information; detecting when AI fabricates clinical findings matters for system safety and helps engineers understand failure patterns

    Expect something like: “You notice the AI added triage vitals not documented in the source chart. What distinguishes this from a legitimate inference, and what specific feedback would help prevent this?”

  2. 2.Documentation pattern analysis

    Systemic gaps in AI output reveal training problems; identifying whether errors are random or template-specific helps product teams prioritize fixes that impact many cases

    Expect something like: “Across 10 trauma cases, the AI consistently omits neurovascular exam findings while capturing other details. How would you frame this to differentiate template issues from annotation problems?”

  3. 3.Guideline edge cases

    Clinical definitions are often contextual; your judgment on ambiguous cases helps create guidelines that reflect real emergency medicine decision-making

    Expect something like: “The guideline defines 'severe allergic reaction' vaguely. A patient with urticaria in one case versus anaphylaxis in another should differ. How would you operationalize this distinction?”

  4. 4.Clinical priority assessment

    AI needs to learn what emergency physicians actually prioritize; your expertise ensures summaries reflect real clinical workflows and decision-making in high-stress environments

    Expect something like: “For a motor vehicle accident, the AI emphasized minor abrasions but omitted head injury protocols. How would you explain why sequence and selection of details matters clinically here?”

How to prepare

  • Study how machine learning teams use physician feedback to improve training and model iteration
  • Gather examples of documentation gaps or inconsistencies you have observed in your emergency medicine experience
  • Research annotation frameworks and structured data formats used in medical AI projects
  • Review case examples where AI clinical summaries differed from what you would document as the ED physician

Facts

Pay
$150–170/hr
Commitment
hourly
Hours
10 per week
Work arrangement
remote · Remote
Eligible locations
USA
Domain
Medicine
Company
Boron
Posted
10/1/2026
Open slots
1