Nurse Practitioner Clinical AI Reviewer

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help clinicians, linguists, and specialists turn short-term annotation projects into a portable portfolio of AI-training work you control and grow into a durable freelance career.

About AI training work

AI training (also called data labeling or annotation) is the human side of building modern models: people review, rate, and create examples that teach AI how to think and respond. This role places experienced clinicians directly in the feedback loop—shaping how medical AI reasons about diagnosis, testing, medications, triage, and escalation.

The role

As a Nurse Practitioner Clinical AI Reviewer you will evaluate AI-generated clinical responses, identify errors or safety risks, and author gold-standard answers and explanations. Your work focuses on clinical reasoning, differential diagnosis, test interpretation, medication safety within scope, care-pathway choice, and identification of red-flag situations.

• Contractor role with flexible, remote hours; OpenTrain is the hiring and contracting organization.
• Labeling work is text-based and includes evaluation ratings and text generation (gold answers).

What you'll do

• Review AI-generated clinical answers for accuracy, safety, clarity, and adherence to the prompt.
• Identify problems in advanced assessment, differential thinking, triage, test ordering/interpretation, and escalation.
• Create detailed prompts and gold-standard answers for assessment, diagnosis, management, patient education, and documentation.
• Evaluate outputs for unsafe assumptions, missing contraindications, inappropriate tests or therapies, and bias.
• Write concise explanations and model solutions that demonstrate correct clinical reasoning.
• Compare and rank multiple model responses for correctness, clarity, and patient-safety implications.

Requirements

You must meet the core clinical and language requirements listed below. All requirements come from the role description; we do not ask you to have experience you don't already hold.

• NP, APRN, or equivalent advanced practice nursing qualification.
• Active licensure or registration, or eligibility, in at least one jurisdiction.
• Strong advanced clinical reasoning across differential diagnosis, triage, red-flag recognition, and escalation.
• Medication safety judgment within scope, including contraindications, interactions, and monitoring plans.
• Minimum C1 English proficiency and exceptional attention to detail.
• Prior experience with AI data training, annotation, clinical documentation review, utilization review, guideline work, or healthcare editorial QA is strongly preferred.

Helpful background

• Family, Adult-Gerontology, Acute Care, Pediatric, Psychiatric-Mental Health, Women's Health, Neonatal, Oncology, Cardiology, Emergency/Trauma, ICU/Critical Care, or Nurse Midwife experience.
• Experience in perioperative care, wound care, diabetes education, community/public health, or similar clinical specialties.
• Comfort evaluating guideline alignment, internal consistency, setting-appropriate care, and patient-safety implications.

Schedule, pay & location

This is remote contractor work with a recommended time commitment of 20+ hours per week. Pay is hourly; the role lists an hourly range.

• Hourly pay: USD $40–90 per hour (PAY_PER_HOUR).
• Employment types: Contractor, Part-time.
• Language: English required (C1).
• Country-specific hiring availability: VN, TL, TH, SG, PK, PH, NP, MY, LK, KH, ID, BT, BD, IN, BR, DE, US.

How it works & who should apply

You will work through OpenTrain as the contracting organization. Tasks are text-based — rating model outputs and composing gold-standard responses — using provided guidelines and examples. This role suits experienced NPs who want flexible, remote work that directly shapes medical AI safety and quality.

To apply, create or use your OpenTrain profile, confirm licensure/qualification details, and submit examples of clinical reasoning or previous review work if available. Selection will focus on clinical expertise and demonstrated judgment rather than years in tech.