$55–65/hr · Mercor · Hourly
Experienced pediatric nurse who evaluates and validates AI-generated clinical outputs against real-world nursing assessment standards and documentation practices.
What you would do
- Review AI assessments and grade them against documented nursing flowsheet data
- Validate completeness and accuracy of AI-generated pediatric nursing assessments
- Annotate and structure inpatient nursing records for AI training datasets
- Identify discrepancies, omissions, and safety risks in model-generated outputs
- Provide structured feedback on assessment quality and documentation alignment
Who they want
- Active RN license (U.S., outside California) with current standing
- Recent pediatric acute care inpatient experience, ideally non-procedural unit
- Competency with comprehensive pediatric nursing assessments and documentation
- Ability to follow detailed annotation guidelines with consistency
- Excellent written communication and responsiveness to feedback
What the interview asks about
1.Assessment completeness evaluation
Your judgment on what constitutes a thorough pediatric assessment is essential for grading AI outputs.
For example: “An AI generates a nursing assessment for a 7-year-old post-op patient that covers vitals and pain but omits fluid intake/output and surgical dressing status. What's the impact?”
2.Pediatric-specific documentation
Understanding what makes documentation appropriate for a child versus an adult directly informs AI evaluation.
For example: “Compare how you would document an assessment for a 2-year-old versus a 16-year-old with the same diagnosis. What differs in your approach?”
3.EHR data validation
Spotting when AI contradicts actual flowsheet entries is fundamental to maintaining data integrity.
For example: “The AI assessment states a patient's last urine output was 4 hours ago, but your flowsheet shows a catheter was placed 1 hour ago. How do you flag this?”
4.Safety risk recognition
Your ability to identify clinically concerning AI outputs protects patient safety in model training.
For example: “An AI generates a recommendation to monitor a patient with severe dehydration every 4 hours. Why might you flag this as unsafe?”
5.Annotation consistency
Following guidelines precisely and asking for clarification when unsure improves data quality for model training.
For example: “You encounter an annotation scenario that doesn't fit the provided guidelines. How do you handle it?”
A task you may get
Review a de-identified pediatric inpatient chart with AI-generated assessment; validate against flowsheet data, identify missing elements, flag safety concerns, and provide structured feedback.
How to prepare
- Gather 2-3 recent pediatric patient charts you assessed and identify key documentation elements
- Review common pediatric assessment pitfalls (vital sign interpretation, developmental red flags)
- Reflect on a case where an omission in documentation could have affected patient safety
- Prepare examples of how your documentation differs by patient age group
The facts
- Pay
- $55–65/hr
- Hours
- Hourly
- Where
- Remote
- Open to
- USA
- Field
- Medicine
- Project name
- Boron
- Posted
- 9/1/2026
- Places left
- 9
We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.