Pediatric RN - Clinical Evaluator

Mercor

New York (NY)

On-site

USD 70,000 - 100,000

Full time

8 days ago
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Job summary

Mercor in New York is seeking experienced Pediatric Inpatient Nurses (RNs) to help train and evaluate AI systems used in clinical settings. This role leverages frontline bedside experience to improve AI tool accuracy, safety, and reliability.

You will annotate, structure, and provide expert feedback on nursing assessments, following detailed guidelines and collaborating with technical teams to refine AI models.

Qualifications

  • Active RN license in the U.S. (excluding California).
  • Recent pediatric acute care inpatient bedside experience, ideally within the past 10 years, with priority given to current or recent documentation experience.
  • Comfortable performing and documenting comprehensive pediatric nursing assessments.

Responsibilities

  • Review and evaluate AI-generated clinical outputs based on nursing flowsheet documentation.
  • Validate accuracy, completeness, and adherence to current documentation standards.
  • Annotate and structure inpatient pediatric nursing assessment data for AI training datasets.
  • Provide expert feedback on nursing assessments and documentation practices.
  • Identify gaps, inconsistencies, or risks in AI-generated responses.
  • Ask clarifying questions when annotation guidance is ambiguous, and contribute to guideline refinement.
  • Collaborate with technical teams to improve model performance.
  • Contribute to the development of high-quality clinical benchmarks.

Skills

RN license
Pediatric inpatient experience
Clinical documentation
Written communication
Deadline oriented
Independent worker

Tools

Epic EHR

Job description

Overview

We’re hiring experienced Pediatric Inpatient Nurses (RNs) to help train and evaluate AI systems used in clinical and healthcare settings. This role is ideal for nurses who want to apply their frontline pediatric bedside experience to improve the accuracy, safety, and reliability of medical AI tools.

You’ll work on projects that require deep clinical judgment, attention to detail, and the ability to translate real-world bedside documentation practices into structured feedback for AI systems.

Key Responsibilities
  • Review and evaluate AI-generated clinical outputs based on nursing flowsheet documentation

  • Validate accuracy, completeness, and adherence to current documentation standards

  • Annotate and structure inpatient pediatric nursing assessment data for AI training datasets

  • Provide expert feedback on nursing assessments and documentation practices

  • Identify gaps, inconsistencies, or risks in AI-generated responses

  • Ask clarifying questions when annotation guidance is ambiguous, and contribute to guideline refinement

  • Collaborate with technical teams to improve model performance

  • Contribute to the development of high-quality clinical benchmarks

Basic Qualifications
  • Active RN license (U.S., outside California)

  • Recent pediatric acute care inpatient bedside experience, ideally in a non-procedural inpatient unit. Candidates should have worked in an inpatient bedside role within the past 10 years, with current or more recent documentation experience prioritized over total years of nursing experience.

  • Comfortable performing and documenting comprehensive pediatric nursing assessments

  • Comfortable following detailed annotation guidelines consistently

  • Excellent written communication skills and responsiveness to feedback

  • Ability to work independently and meet deadlines

Preferred Qualifications
  • Epic EHR experience (most common platform, closely aligns with many workflows)

  • Comfortable learning new annotation tools and web-based platforms

  • Able to navigate transcripts efficiently and use AI-assisted tools appropriately while still verifying outputs

  • Experience reviewing charts for quality improvement, utilization review, CDI, or clinical informatics

  • Previous annotation, chart abstraction, or healthcare AI experience

  • Comfortable discussing edge cases and contributing to guideline refinement

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