Mindrift is offering a remote project-based Claims Processing Agent role as a freelance AI trainer focused on testing and improving AI for insurance. You'll evaluate auto claim decisions for accuracy and regulatory compliance, design FNOL scenarios, write and grade fraud-flagging cases, build subrogation test cases using state negligence rules, and document results with policy citations and payout calculations. Candidates with 3+ years in claims or related fields, a degree, familiarity with auto coverage decisions and negligence rules, and strong written English (C1+) are preferred; CPCU or AIC credentials are a plus but not required. Apply with an English CV and indicate your English proficiency; expect 10-20 hours/week during active phases and earnings up to $60/hour depending on scope.
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:
What we look for
This opportunity is a good fit for professionals with a background in insurance claims, legal services, or broader financial services who are open to part-time, non-permanent projects. Ideally, contributors will have:
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.