Remote Population Health AI Quality Auditor

Alignerr Corp.; United States; Remote; AUD 14,000 - 55,000; Part time

Job summary

Alignerr Corp. is seeking a Population Health Informaticist to support AI training projects focused on population health data. This fully remote, hourly contract role suits experts who can evaluate AI-generated analyses and ensure clinical relevance.

You will work asynchronously 10–40 hours per week, reviewing dashboards, data pipelines, and reporting tools, translating complex concepts into clear insights for health stakeholders, and identifying errors or gaps in AI outputs.

Qualifications

• Hands-on experience working with healthcare, public health, or population-level datasets.
• Strong analytical skills and familiarity with data visualization and reporting tools.
• Ability to assess the quality of data-driven insights and health metrics.
• Comfortable evaluating written and visual content for accuracy and clarity at scale.
• Detail-oriented with a systematic approach to quality assessment.
• Clear written communication skills.

Responsibilities

• Evaluate and improve AI-generated analyses of population health data, including trends, disparities, and intervention opportunities.
• Review AI outputs for accuracy, clinical relevance, and methodological soundness.
• Provide structured feedback on dashboards, data pipelines, and reporting frameworks generated by AI systems.
• Translate complex analytical concepts into clear, actionable insights that help AI communicate effectively to health stakeholders.
• Flag errors, gaps, or misleading interpretations in AI-generated population health content.
• Work independently and asynchronously on your own schedule.

Job description

Alignerr Corp. is seeking a Population Health Informaticist to support AI training projects focused on population health data. This fully remote, hourly contract role suits experts who can evaluate AI-generated analyses and ensure clinical relevance.

You will work asynchronously 10–40 hours per week, reviewing dashboards, data pipelines, and reporting tools, translating complex concepts into clear insights for health stakeholders, and identifying errors or gaps in AI outputs.