Inpatient RN - Clinical Annotator

Mercor; New York (NY); On-site; USD 21,000 - 35,000; Full time

Job summary

Mercor is seeking an experienced inpatient RN to help train and evaluate AI systems used in clinical settings. You will translate bedside documentation into structured feedback to improve AI tools and ensure accuracy and safety.

The role requires recent acute-care RN experience, strong clinical judgment, and the ability to work at least 10 hours per week with reliable documentation practices. Collaboration with engineers and clinical scientists is essential.

Qualifications

• Active RN license (US, outside California)
• Recent acute-care inpatient bedside RN experience within last 5 years
• Broad clinical exposure across inpatient populations
• Excellent written communication and responsiveness to feedback
• Able to dedicate a minimum of 10 hours per week

Responsibilities

• Review and evaluate AI-generated clinical outputs based on nursing documentation
• Validate accuracy, completeness, and adherence to current documentation standards
• Annotate and structure inpatient 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
• Investigate ambiguous clinical questions and provide thoughtful recommendations
• Surface issues and opportunities for improvement and contribute to guideline refinement
• Collaborate with engineers, PMs, and clinical scientists to improve model performance

Skills

Active RN license

Acute-care RN

Excellent written comms

Independent worker

10 hours/week availability

Tools

Epic EHR

Job description

We're hiring experienced inpatient Registered 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 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 documentation

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

• Annotate and structure inpatient 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

• Take an ambiguous clinical question, investigate it, and return a thoughtful recommendation

• Proactively surface issues, patterns, and opportunities for improvement, and contribute to guideline/taxonomy refinement

• Support related clinical product work as needs evolve (annotation, clinical review, model evaluation, user-feedback investigation)

• Collaborate with engineers, PMs, and clinical scientists to improve model performance

Basic Qualifications

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

• Recent acute-care inpatient bedside RN experience within the last 5 years (current documentation experience is prioritized over total years of nursing experience)

• Broad clinical exposure: comfortable reviewing documentation across different inpatient populations, not overly specialized

• Comfortable performing and documenting comprehensive nursing assessments

• Comfortable following detailed annotation guidelines consistently

• Self-starter with high ownership who can work independently and meet deadlines

• Excellent written communication skills and responsiveness to feedback

• Able to reliably and consistently dedicate a minimum of 10 hours per week

Preferred Qualifications

• Experience at a large academic or teaching health system

• Previous data annotation, chart abstraction, or healthcare-AI experience with positive performance reviews

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

• Comfortable learning new annotation tools and web-based platforms

• Curious and adaptable: interested in how the product/model works and comfortable with processes that change quickly