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Health AI Conversation Evaluator

Job in San Bernardino, San Bernardino County, California, 92401, USA
Listing for: Mercor
Full Time position
Listed on 2026-10-02
Job specializations:
  • IT/Tech
    AI Evaluation, Data Annotation/ AI Labeling
Salary/Wage Range or Industry Benchmark: 20 - 160 USD Hourly USD 20.00 160.00 HOUR
Job Description & How to Apply Below

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark
, General Catalyst
, Peter Thiel
, Adam D'Angelo
, Larry Summers
, and Jack Dorsey
.

Position: Generalist Annotator — Health AI Conversation Quality Evaluation
Type:
Contract
Compensation: $20–$160/hour
Location:
Remote

Role Responsibilities
  • Evaluate the relevance and clarity of AI responses
    . Ensure the AI answers the question in understandable language for non-experts.
  • Detect sycophancy in AI interactions. Identify when the AI tells users what they want to hear instead of what they need.
  • Assess the tone and outcome of conversations. Determine if the user leaves informed and calm or anxious and uninformed.
  • Provide written justification for ratings below the top option. Explain what went wrong and suggest improvements.
  • Work independently to review conversations ranging from 1 to 7 turns. Spend 2–13 minutes per conversation based on length.
Qualifications Must-Have
  • Strong written English skills.
  • Ability to explain judgments in two to four specific sentences.
  • Careful reading to notice omissions in responses.
  • Comfort with applying a detailed rubric consistently.
  • Reliable availability for time-boxed batches.
Preferred
  • Experience in annotation, human-feedback, evaluation, or content review.
  • Background in explaining technical concepts to non-experts.
  • Experience with rubric-based grading or quality assurance.
Important
  • Using an AI tool to write comments is prohibited. This work captures human judgment that AI lacks. AI-written feedback corrupts the dataset and is checked for.
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