Healthcare & Social Assistance Specialist - Freelance AI Trainer Project
$10-30/hr as listed
- Listed pay
- $10-30/hr as listed
- Field
- Medicine
- Languages
- English
- Where
- United States
- Type
- Contract, remote
- Posted on Meridial
- First seen here
- 9 October 2026
Summary
We are sourcing independent Healthcare and Social Assistance Specialists to provide their expertise for an AI benchmark evaluation project. As AI models increasingly generate professional-grade...
From the Meridial listing
Project Overview
We are sourcing independent Healthcare and Social Assistance Specialists to provide their expertise for an AI benchmark evaluation project. As AI models increasingly generate professional-grade medical summaries, patient care workflows, and social service documentation, their accuracy relies entirely on robust, expert-crafted training data. The objective of this project is to autonomously produce high-quality evaluation tasks, strong prompts, and clear, well-structured rubrics that generate clean, reliable data for model training.
Project Deliverables & Scope
Operate autonomously to design complex evaluation frameworks and provide structured training data. Expected deliverables include:
- Task & Prompt Creation: Generating realistic, high-quality prompts that compel the AI model to produce complex, professional-grade deliverables specific to healthcare operations, clinical guidelines, and social assistance programs.
- Rubric Development: Writing clear, well-structured evaluation rubrics with criteria that are highly specific, non-ambiguous, and easy to score.
- Benchmark Evaluation Data Generation: Producing clean, reliable training data that directly aids in the evaluation and refinement of AI models handling complex health and social care-related tasks.
- Quality Assurance & Fact-Checking: Ensuring all generated tasks and scoring criteria reflect strict, real-world clinical standards, medical accuracy, and social service regulatory guidelines.
Required Expertise
To successfully fulfill the deliverables of this project, Contractors must possess deep industry knowledge to craft realistic professional scenarios.
Core skillset includes:
- Demonstrable professional expertise within the healthcare, nursing, social work, or allied health sectors, with a deep understanding of medical terminology, patient care standards, and social assistance frameworks.
- Strong writing and prompt generation skills, with the ability to design highly realistic, complex healthcare task scenarios for AI evaluation.
- Proficiency in rubric generation, specifically the ability to create objective, non-ambiguous scoring criteria that leave no room for subjective interpretation.
- A meticulous, detail-oriented approach to fact-checking medical literature, clinical protocols, and social service policies to generate reliable data for system benchmarking.
We offer a pay range of $10-to-$30 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor you’ll supply a secure computer and high‑speed internet; company‑sponsored benefits such as health insurance and PTO do not apply.
Engagement Type: Freelance / Independent Contractor
Workplace Type: Remote
Text above is the platform's own listing, shown as published. Check the details on Meridial before applying.
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Practice questions and what each platform says about its screening for this kind of role:
Terms in this listing
- Benchmark
- A fixed set of test tasks used to compare models or track progress over time.
- Prompt
- The input given to a model: a question, an instruction or a conversation so far.
- Rubric
- A written list of criteria and scores used to judge a response, for example accuracy, instruction following and tone, each with clear pass or fail descriptions.
- Guidelines
- The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
- Fact-checking
- Checking each claim in a model's answer against reliable sources and marking what is wrong or unsupported.
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