AI Work Central

Nurse

SME CareersMedicineFirst seen 9 Oct 2026

up to$65/hr as listed by SME Careers, USDEstimate a week at this rate
Apply on SME Careers

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Where
17 countries

United States, Germany, Brazil, India, Bangladesh, Bhutan, Indonesia, Cambodia, Sri Lanka, Malaysia, Nepal, Philippines, Pakistan, Singapore, Thailand, Timor Leste, Vietnam

Language
English
Type
Contract, remote

Posted on SME Careers First seen 9 Oct 2026Checked 12 Oct 2026

From the SME Careers listing

In this hourly, remote contractor role, you will work as a General Nurse Subject Matter Expert (SME) to review AI-generated nursing and clinical-care responses and/or create expert healthcare content, evaluating reasoning quality and step-by-step clinical problem-solving while providing precise written feedback. You will assess solutions for accuracy, clarity, safety, and adherence to the prompt; identify errors in clinical assessment, triage, escalation/red-flag recognition, medication safety basics, patient education, infection control, and care planning; fact-check healthcare information; write high-quality explanations and model solutions that demonstrate correct reasoning; and rate and compare multiple responses based on correctness and reasoning quality.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your healthcare expertise directly helps improve the world’s premier AI models by making their clinical and public-health reasoning more accurate, reliable, and clearly explained.

Important:

There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Responsibilities

  • Develop AI Training Content: Create detailed prompts and gold-standard answers across nursing care topics (assessment, triage, safety, education, documentation).
  • Optimize AI Performance: Evaluate and rank AI responses to enhance accuracy, safety, clarity, and contextual relevance.
  • Ensure Model Integrity: Test AI outputs for potential inaccuracies, unsafe guidance, and bias; validate reliability across use cases.

Requirements

  • RN (or equivalent nursing qualification) with active licensure/registration (or eligibility) in at least one jurisdiction.
  • 3-5+ years of professional nursing experience in a clinical or community setting.
  • Strong clinical assessment and prioritization skills: vital signs interpretation, red-flag recognition, escalation pathways, and safe handoffs.
  • Solid understanding of common disease processes and nursing care interventions across acute and/or outpatient settings.
  • Excellent attention to detail when fact-checking and identifying unsafe assumptions, missing contraindications/precautions, or inappropriate advice; minimum C1 English proficiency.
  • Comfortable evaluating answers for internal consistency (timelines, symptoms, monitoring frequency, dosing logic checks at a nursing level) and appropriateness for setting (ED vs ward vs outpatient).
  • Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
  • Previous experience with AI data training/annotation, clinical documentation review, utilization review, nursing education, or healthcare editorial QA is strongly preferred.

Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.

Prepare for the assessment

Practice questions and what each platform says about its screening for this kind of role:

Terms in this listing

  • Prompt: The input given to a model: a question, an instruction or a conversation so far.
  • Assessment: Any screening step a platform uses before giving you work: a quiz, a skills test, a language test, a writing sample or an AI interview.
  • Relevance: How well a result or answer matches what the user was looking for.
  • Fact-checking: Checking each claim in a model's answer against reliable sources and marking what is wrong or unsupported.
  • Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.

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