Medical General Expert
SME CareersMedicineFirst seen 9 Oct 2026
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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
- Field
- Medicine
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 review AI-generated medical responses and/or generate 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, and adherence to the prompt; identify errors in clinical methodology or conceptual understanding; fact-check medical 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 in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
- Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
- Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.
Requirements
- Bachelor’s degree (or higher) in Medicine (MD/DO), Nursing, Public Health (MPH), Health Sciences, or Allied Health, with strong grounding in Epidemiology, Clinical Medicine, Healthcare Systems, and Patient Care.
- 5+ years of professional experience in Nursing, Public Health, Health Sciences, or Allied Health.
- Confident in clinical reasoning (differential diagnosis, risk stratification, red-flag recognition) and explaining why a conclusion follows from the evidence.
- Strong understanding of disease processes, patient care concepts, public health principles, healthcare systems, and medical terminology.
- Exceptional attention to detail when fact-checking medical content and identifying unsafe assumptions, missing contraindications, or misinterpretation of tests; Minimum C1 English proficiency.
- Comfortable evaluating answers for internal consistency (timelines, physiology, dosing logic), appropriateness for setting (ED vs outpatient), and patient safety implications.
- 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, 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.
- Relevance: How well a result or answer matches what the user was looking for.
- Grounding: Whether an answer is supported by a given source, such as a document, search result or retrieved passage.
- 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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