Job Description
Contract · Fully remote · $55–75/h
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nROLE OVERVIEW
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- Huzzle is seeking insurance professionals to test how well frontier AI systems handle real underwriting, claims, actuarial and regulatory work, and to document where they fall short.n
- You will not be underwriting or adjusting claims. You will be finding the point at which an AI system stops being reliable at this work, and proving that point objectively.n
- Insurance is an unusually strong domain for this: policy wordings, regulatory returns and published filings are accurate, public and checkable, which makes model errors easy to evidence and hard to argue with.n
- Each submission has three parts: a realistic technical request, documented evidence of where the model failed it, and a scoring rubric that lets any reviewer grade a future attempt consistently.n
- Full training and worked examples are provided. Prior AI or data-annotation experience is not required.n

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nKEY RESPONSIBILITIES
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- Design realistic insurance tasks: policy wording and coverage comparison,



exposure and aggregation analysis, claims reserving and triage, rating and pricing analysis, reinsurance structure comparison, regulatory disclosure summarisation.n
- Ensure each task requires live research against primary sources — published policy wordings, regulator and supervisory publications, public rate filings, solvency and financial condition disclosures, market and association statistics — plus multi-step calculation and an actual file deliverable. Typically an Excel workbook or technical memo, but also PDFs, decks and CSVs.n
- Test each task against a frontier AI model and examine its output with the scrutiny you would apply before signing it off.n
- Document exactly where and how it failed: coverage terms or exclusions asserted that do not appear in the cited wording, jurisdictions conflated, requirements attributed to the wrong regulatory regime, reserving or rating arithmetic that does not r

📌 Insurance Expert (New Delhi)
🏢 Huzzle.com
📍 New Delhi