Senior Auto Claims Adjuster – Freelance AI Trainer

Remote from: USA
Salary, yearly, USD: 25,000 - 120,000
Department: Business Development
Employment type: Full Time,
Job posted: 21 Jun 2026
Apply before: 21 Jul 2026
Experience level: Senior
Views / Applies: 13 / 2

Mindrift connects AI experts and clients to advance Generative AI models.

Actively Hiring

AI Summary

This freelance role involves training AI systems by evaluating and creating test scenarios for auto insurance claims. Contributors need deep knowledge of auto claims adjusting, including coverage decisions, fraud detection, and state-specific regulations. Tasks are project-based with 10-20 hours per week, offering flexibility but no permanent employment. Ideal candidates have 3+ years of hands-on experience and familiarity with U.S. state rules. Compensation is variable, with potential earnings up to $60 per hour based on performance.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight This role requires senior-level expertise in auto claims adjusting, including deep knowledge of state-specific regulations, coverage decisions, and fraud detection, making it challenging and suitable only for experienced professionals.

Salary Analysis

AI Insight The offered salary range of $25,000 to $120,000 is broad, reflecting the freelance nature and variable hours. At $60/hour for 10-20 hours/week, annual compensation could range from $31,200 to $62,400, but the maximum end suggests potential for full-time equivalent earnings. The median of $72,500 is competitive for a senior adjuster in the freelance market.

Key Skills

Auto Claims Claims Adjusting Fraud Detection Coverage Decision State-Specific Regulations Policy Analysis AI Training Test Case Creation Subrogation Negligence Rules

Cover Letter Sample

I am writing to express my interest in the Senior Auto Claims Adjuster - Freelance AI Trainer position. With over 5 years of experience in auto claims adjusting, including handling complex coverage decisions and fraud detection, I am confident in my ability to contribute to your AI training projects.

I hold an AIC designation and have deep knowledge of state-specific regulations, comparative and contributory negligence rules, and policy citation. I am skilled in creating test scenarios that challenge AI systems and evaluating outputs for accuracy and compliance.

I am comfortable working independently on a project basis and can commit 10-20 hours per week. I am excited about the opportunity to apply my expertise to improve AI performance in the insurance domain.

Thank you for considering my application. I look forward to the possibility of contributing to your team.

Possible Interview Questions

How would you design a test case to evaluate an AI's ability to distinguish between collision and comprehensive coverage?

I would create a scenario where a vehicle is damaged by a falling tree while parked, then also hit a curb while leaving the scene. The AI should correctly identify the tree damage as comprehensive and the curb damage as collision. The test case would include contradictory information such as the driver's report stating both incidents occurred simultaneously, and the policy language defining collision and comprehensive. I would expect the AI to reference specific sections of the policy and apply the correct coverage.

Describe a complex subrogation scenario you would create for AI testing.

I would design a multi-vehicle accident where the insured is 40% at fault under comparative negligence rules. The AI must calculate the subrogation amount based on state law, considering policy limits and deductibles. For example, total damages $50,000, insured's coverage $30,000, and the other driver's insurer demands full payment. The AI should correctly apply the negligence percentage and determine the maximum recovery.

How do you ensure a test case for fraud detection is realistic and challenging?

I would incorporate common fraud indicators like late reporting, recently increased coverage, and inconsistent damage descriptions. The scenario would include a claimant reporting a theft but the vehicle is found with damage matching a prior accident. The AI must flag the claim using structured reason codes and recommend SIU referral without being misled by plausible explanations.

Explain how you would evaluate an AI's reservation-of-rights letter generation.

I would provide a scenario where a coverage issue arises, such as a policy exclusion for intentional acts. The AI must draft a letter that reserves rights without admitting liability. I would check for appropriate language that stays within the bad-faith line, includes specific policy citations, and clearly states the investigation is ongoing. The letter should not imply denial prematurely.

What is your approach to validating coverage-limits math with multiple endorsements?

I would create a test case with a base policy limit of $50,000, plus an OEM endorsement adding $10,000, a rideshare endorsement adding $20,000, and an extended rental endorsement adding $5,000. After a loss, the AI must stack these correctly and compute the total available coverage. I would ensure the AI applies any sub-limits or deductibles in the correct order and produces the exact payout.

Please submit your CV in English and indicate your level of English proficiency.

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.

What this opportunity involves

While each project involves unique tasks, contributors may:

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.

What we look for

This opportunity is a good fit for mortgage underwriters and loan origination professionals open to part-time, non-permanent projects. Ideally, contributors will have:

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;
Associate in Claims (AIC), CPCU, CIFI, or SCLA credential is a strong positive signal — but not required if experience is solid;
Strong written English (C1+).

How it works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Project time expectations

For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation

On this project, contributors can earn up to $60 per hour equivalent, depending on their level and pace of contribution.

Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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