Senior Auto Claims Adjuster - Freelance AI Trainer

Mindrift•Miami, FL

About The Position

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. This opportunity is a good fit for mortgage underwriters and loan origination professionals open to part-time, non-permanent projects.

Requirements

Degree in Finance, Business, Insurance, or related field — or equivalent professional experience; no specific degree is required if AIC, CPCU, or comparable credentials are present, or if the candidate has 4+ years of hands-on claims adjusting experience
3+ years of hands-on auto claims adjusting, examining, or supervisory experience at a U.S. carrier, independent adjusting firm, or SIU team
Ability to make coverage decisions (collision vs. comprehensive, endorsement stacking, exclusion vs. coverage grant) without looking them up
Familiarity with U.S. state-specific rules — comparative vs. contributory negligence states, state adjuster licensing requirements
Experience reading full auto policy documents with citation discipline (able to reference a specific section, e.g. "Section IV.B.2")
Comfort computing payout math involving deductibles, sub-limits, and layered endorsements in Excel or equivalent
Awareness of the bad-faith line and adjuster authority-limit culture
Strong written English (C1+)

Nice To Haves

Associate in Claims (AIC), CPCU, CIFI, or SCLA credential is a strong positive signal — but not required if experience is solid

Responsibilities

Evaluate AI-generated auto insurance claims decisions for accuracy, coverage correctness, and regulatory compliance
Design realistic FNOL (First Notice of Loss) scenarios with deliberate contradictions, decoy files, and outdated documents to test agent robustness
Create test cases for coverage-scope decisions (collision vs. comprehensive) where the correct answer requires domain knowledge, not keyword matching
Write and grade fraud-flagging scenarios using structured reason codes (late reporting, recently purchased policy, inconsistent damage) for SIU referral
Build subrogation test cases applying state-specific negligence rules (comparative vs. contributory) and assess likelihood of recovery
Develop supervisor-escalation scenarios that test whether the agent correctly recognizes authority-limit thresholds ($25,000) and stops short of auto-approving
Draft and evaluate reservation-of-rights letter scenarios, verifying language stays within the bad-faith line
Validate coverage-limits math when multiple endorsements (OEM, rideshare, extended rental) stack on a single claim
Document test cases clearly with correct answers, policy citations, and payout calculations