Registered Nurse
Listing checked October 10, 2026 · pay as published by Micro1
Overview
micro1 is bringing on Registered Nurses to support a project that trains next-generation AI systems using real inpatient clinical knowledge. You will work with nursing notes, progress notes, care plans, orders, lab results, and discharge summaries, checking them for accuracy and completeness. The role also involves reviewing AI-generated clinical content for safety and soundness across different inpatient populations. No AI background is needed. Your bedside experience and nursing judgment are the core qualifications.
What You'll Do5
- 1Annotate and review inpatient clinical documentation, including nursing notes, progress notes, care plans, orders, lab results, and discharge summaries, for accuracy and completeness.
- 2Assess AI-generated clinical outputs for correctness, safety, and suitability across varied inpatient care scenarios and patient populations.
- 3Use bedside nursing judgment and familiarity with inpatient workflows, standards of care, and documentation practices to validate and enrich datasets.
- 4Communicate with project stakeholders in writing and in conversation to clarify requirements and resolve unclear points in clinical documentation.
- 5Give detailed feedback on guidelines and edge cases to improve project workflows and how models interpret clinical material.
Requirements6
- 1Based in the United States and able to practice as a Registered Nurse in the U.S.
- 2Recent inpatient bedside RN experience.
- 3Broad clinical exposure, with comfort reviewing documentation across different inpatient populations rather than a narrow specialty.
- 4Experience in an academic or large health system setting, such as a large teaching hospital, with exposure to complex patients, workflows, and documentation practices.
- 5Previous annotation experience with positive performance reviews.
- 6Strong written and verbal communication skills, with close attention to clarity and detail.
Who Should Apply
This role fits a U.S.-based Registered Nurse who has recent inpatient bedside experience and can review documentation across a wide range of inpatient populations. Nurses from large teaching hospitals or academic health systems will find the work familiar because it involves complex patients, varied workflows, and detailed documentation. Candidates with prior annotation experience and positive performance reviews are especially well matched. The role is less suitable for nurses who have only worked in a single narrow specialty or who have been away from bedside care for a long time. A common reason candidates score low on fit is limited inpatient documentation review experience, since the work depends on reading and evaluating notes, care plans, and discharge summaries. Another frequent issue is weak written communication, because the role requires clear feedback on guidelines and edge cases.
Salary Insight
The role pays $60 to $70 per hour. This is a contract position, and the rate is stated upfront.
Pay and demand for AI Training roles
AggregatedTypical pay
$70/hour
This role
$60–$70/hr
Most AI Training roles pay $36–$95 per hour. This role's pay falls inside that range.
Based on 972 similar roles that publish pay · 300 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 1,103
- Listed in last 30 days
- 573
- Remote
- 99%
Hiring most right now: micro1 (289) · SME Careers (253) · Mercor (136)
Most requested skills · share of roles
- ai training15%
- ai evaluation13%
- llm evaluation12%
- data annotation11%
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Compensation
$60–70/hr
Required Skills
Application Tip
In your application, highlight recent inpatient bedside RN experience and name the specific documentation types you have reviewed, such as nursing notes, care plans, and discharge summaries. If you have annotation experience, mention it and note any positive performance reviews. Also state your U.S. RN license and any large teaching hospital or academic health system background, since those details directly match the preferred qualifications.
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