Training Turk

Senior Multiline Claims Examiner

$80/hr · Mercor · Hourly, 20 hours a week

An experienced claims professional shapes AI model training by creating realistic claims scenarios and evaluating model outputs for accuracy and sound judgment.

What you would do

  • Design authentic claims scenarios involving intake, investigation, coverage assessment, damage evaluation, negotiation, settlement, and case closure
  • Write reference-quality claims decisions with clear justification that serve as training examples for models
  • Score AI-generated claim handling responses using rubrics focused on accuracy, coverage understanding, and procedural completeness
  • Document errors found in model outputs, identifying investigation gaps, valuation mistakes, or coverage misapplication

Who they want

  • Minimum 2+ years hands-on professional experience adjusting, examining, or handling property and casualty claims
  • Demonstrated expertise handling claims in at least one specialty (property damage, auto physical damage, bodily injury, commercial liability, catastrophe, specialty P&C)
  • Strong understanding of the claims process lifecycle and ability to articulate reasoning in writing for AI researchers
  • Professional credentials preferred, such as AIC, CPCU, SCLA, or active adjuster license; multiline or specialized experience is a plus

Main skills

Claims handlingClaim investigationDamage evaluation

What the interview asks about

  1. 1.Claims process understanding

    You'll evaluate whether AI reasons correctly about claims procedures; missing knowledge about proper sequence or requirements produces poor training feedback.

    For example: “A commercial property claim comes in after a business fire. Walk me through the sequence of investigation steps, coverage checks, and valuation decisions you'd document, and explain why skipping any of these matters.”

  2. 2.Policy interpretation and coverage

    Claims adjudication requires matching policy language to facts; your written responses teach models how to apply coverage carefully and identify when escalation is needed.

    For example: “A homeowner claims business interruption loss under their homeowners policy. How would you frame the coverage analysis in your response to make the reasoning clear to an AI system?”

  3. 3.Claims decision rationale

    Your documentation trains the model; vague or incomplete reasoning produces poor quality training data and leaves non-specialists confused about key judgments.

    For example: “You're deciding on a settlement range for a liability claim with multiple injured parties. What factors would you document in your reference response to justify your recommendation?”

  4. 4.Error detection in AI outputs

    You must spot flawed claim reasoning; missing errors means the AI receives inadequate feedback and continues making similar mistakes.

    For example: “An AI response settles a claim for water damage without investigating whether mold damage is covered separately or addressed by exclusions. How would you structure feedback on this gap?”

  5. 5.Multiline or specialty complexity

    Claims vary significantly across lines; depth in one area plus breadth makes your training data more valuable for creating diverse, realistic scenarios.

    For example: “You have experience in property claims. If asked to create scenarios in commercial general liability, what key procedural or coverage differences would you emphasize in your scenarios?”

A task you may get

Create 2 realistic claims scenarios across different claim types or coverage questions. For each, write a reference decision with complete reasoning and identify 2-3 potential errors an AI model might make in handling that scenario.

How to prepare

  • Review recent market trends or regulatory changes affecting claims procedures in your specialty; be ready to explain impact on adjudication decisions
  • Prepare 2-3 complex claims you've handled where coverage interpretation or valuation was non-obvious and required balancing multiple factors
  • Identify common mistakes you've observed in claims handling that signal misunderstanding of procedures, coverage, or judgment
  • Reflect on claims scenarios where the full lifecycle (from intake through closure) created genuine complexity and required careful documentation

The facts

Pay
$80/hr
Hours
Hourly, 20 hours a week
Where
Remote · Remote United States, Canada, or United Kingdom
Open to
USA, CAN, GBR, AUS, NZL, IRL, MLT, NLD, SWE, DNK, NOR, FIN, DEU, AUT, BEL
Field
Miscellaneous
Posted
8/25/2026
Places left
14

We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.