About this role
In this hourly, remote contractor role, you will work as a Public Health Subject Matter Expert (SME) to review AI-generated public health and population-health responses and/or create expert public health content, evaluating reasoning quality and step-by-step analytical problem-solving while providing precise written feedback. You will assess solutions for accuracy, clarity, safety, and adherence to the prompt; identify errors in epidemiologic reasoning, study interpretation, surveillance logic, risk communication, and health-systems concepts; fact-check public health claims; 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 public health expertise directly helps improve the world’s premier AI models by making their population-health reasoning, evidence interpretation, and public-facing guidance 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.
Requirements
- Bachelor’s degree (or higher) in Public Health (MPH preferred), Epidemiology, Biostatistics, Health Policy, Global Health, or a related field (or equivalent professional experience).
- 5+ years of professional experience in public health (government/public sector, NGOs, academia, healthcare systems, global health, consulting, or similar).
- Confident in public health reasoning (problem framing, causal thinking, bias/confounding awareness, risk stratification, and prioritization).
- Strong understanding of disease processes at the population level, prevention strategies, screening concepts, vaccination principles, and health behavior frameworks.
- Excellent attention to detail when fact-checking and identifying unsafe assumptions, misleading causal claims, incorrect interpretation of statistics, or inappropriate recommendations; minimum C1 English proficiency.
- Comfortable evaluating answers for internal consistency (denominators, rates vs risks, time horizons, surveillance definitions, and uncertainty).
- Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
- Prior experience with AI data training/annotation, guideline work, systematic reviews, program evaluation, policy analysis, or editorial QA is strongly preferred.
Responsibilities
- Develop AI Training Content: Create detailed prompts and gold-standard answers across epidemiology, public health practice, policy, prevention, and risk communication topics.
- Optimize AI Performance: Evaluate and rank AI responses to improve correctness, clarity, contextual relevance, and scientific rigor.
- Ensure Model Integrity: Test AI outputs for inaccuracies, misleading evidence interpretation, unsafe recommendations, and bias; validate reliability across populations and settings.
Skills
- Outbreak Investigation
- Disease Surveillance
- Biostatistics
- Health Policy
- Risk Communication
- Systems
- Program Evaluation
- Evidence-Based Public Health
- Epidemiology
- English
- Health Equity
Additional skills
- Public Health
- Epidemiology
- Biostatistics
- Health Policy
- Global Health
- Population Health
- Disease Surveillance
- Outbreak Investigation
- Program Evaluation
- Monitoring & Evaluation (M&E)
- Systematic Reviews
- Evidence Synthesis
- Clinical Guidelines
- Health Economics
- Health Behavior Change
- Health Promotion
- Vaccination Programs
- Screening Programs
- Risk Stratification
- Causal Inference
- Confounding & Bias Assessment
- Statistical Analysis
- Data Interpretation
- Research Methods
- Scientific Writing
- Editorial QA
- Fact-Checking
- Quality Assurance
- AI Data Annotation
- Literature Review
