Join OpenTrain to help design taxonomies, rubrics, and benchmarks for detecting self-harm, eating disorders, and suicide risk in AI systems; remote, contractor role (20+ hrs/week) paying USD $50–$90/hr, English required.
Medical & Health
100% Remote Hourly · $50–$90/hr
$50–$90/hr
Compensation
Worldwide
Eligibility
Entry
Experience
Jun 30, 2026
Posted
Open worldwide
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OpenTrain is the #1 platform for people who build careers in AI training and data labeling. We help experts find and apply to remote projects, consolidate work into a single portfolio, and grow a durable freelance career teaching AI how to behave safely and ethically.
Why AI training matters for mental-health safety
AI systems learn from examples prepared and reviewed by people. For safety-sensitive domains like mental health, clinicians and crisis experts create the taxonomies, evaluation rubrics, and benchmarks that teach models to recognize risk, avoid harmful advice, and guide safer responses.
This role puts clinicians and crisis-care specialists at the center of how safety tooling is built — your annotations and frameworks help models identify crisis signals and shape intervention standards for adolescents and adults.
The role
You will work remotely as a subject-matter contributor building and validating taxonomies, rubrics, and benchmark guidelines for self-harm, eating disorders, emotional dependency, and suicide prevention. Your contributions will include reviewing and classifying real-world scenarios to inform clinical response standards and AI safety evaluations.
Labeling activities will focus on text data and include classification, evaluation/rating, and data-collection style tasks to support safety tooling and benchmark creation.
Population focus: adolescent and adult mental-health scenarios.
What you'll do day-to-day
Develop structured taxonomies for self-harm, eating disorders, emotional dependency, and suicide prevention.
Design frameworks to detect harmful advice, crisis signals, and high-risk language in digital environments.
Create and validate rubrics for clinical response standards and crisis intervention protocols.
Establish benchmark guidelines to evaluate risk, support vulnerable individuals, and minimize harm.
Review, annotate, and classify real-world scenarios with high accuracy and ethical alignment.
Contribute expert insight to mental-health safety evaluation tools used for model assessment.
Requirements and qualifications
This role requires clinical experience and direct crisis-care background. The position lists entry-level experience level in the posting but expects demonstrable clinical safety expertise.
Significant crisis-care and clinical safety experience; direct practice in crisis response or adolescent mental health.
Experience creating clinical documentation, guidelines, or evaluation frameworks.
Strong written and verbal communication skills.
Familiarity with clinical risk assessment, digital mental-health platforms, or relevant research is a plus.
Comfort reviewing and classifying real-world safety scenarios for AI training.
Language: English required.
Compensation, schedule, and engagement
This is a part-time contractor role with a minimum commitment of 20+ hours per week. OpenTrain engages contributors worldwide.
Pay is per hour (PAY_PER_HOUR) in USD; the posting lists an hourly range of $50–$90/hr (hourlyRate shown as $90, with minimum and maximum $50–$90).
Employment type: Contractor, Part-time.
Time requirement: 20+ hours/week.
Worldwide contributors accepted; English proficiency required.
Who should apply and next steps
Apply if you are a clinician or crisis-care specialist who wants to shape how AI systems detect and respond to mental-health crises. This role is especially suited to people with hands-on crisis response experience and a track record of producing clinical guidelines or assessments.
To get started, create an OpenTrain profile, review the project details, and submit your application. OpenTrain connects you to work where your clinical judgment directly improves AI safety and supports vulnerable people.
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