Insurance Specialist

Mercor; United States; On-site; USD 140,000 - 200,000; Full time

Job summary

Cincinnatus LLC, leading an AI lab's GenAI team, seeks an Insurance SME to help build foundational AI models. You will evaluate AI outputs against rubrics, design insurance-related tasks, and contribute to rigorous training data development to advance underwriting accuracy.

This W-2 role focuses on domain expertise and real-world underwriting judgment, requiring at least 35 hours per week and superb problem-solving and communication skills.

Qualifications

• 8+ years of dedicated professional experience in insurance at a top-tier organization.
• Hands-on experience evaluating LLM/AI model outputs against rubrics or structured criteria.
• Demonstrable career progression within underwriting/claims risk domains.
• Ability to engage reliably for at least 35 hours/week during weekdays.

Responsibilities

• Guide research and engineering teams to close knowledge gaps in underwriting, claims, and risk assessment reasoning.
• Design challenging, domain-relevant insurance tasks and write accurate, well-reasoned solutions grounded in real underwriting/claims practice.
• Evaluate AI model outputs against structured rubrics and provide clear, written feedback on correctness, judgment, and reasoning quality.
• Develop and refine evaluation guidelines and scoring rubrics specific to insurance tasks.
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data.

Skills

Insurance domain

LLM evaluation

Communication skills

35h/wk availability

Job description

Join a leading AI lab's cutting-edge GenAI team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced Large Language Models.

1. Overview

Join a leading AI lab's cutting-edge GenAI team and help build foundational AI models from the ground up. We're seeking talented Insurance subject-matter experts (SMEs) with deep domain expertise and hands‑on experience evaluating AI model outputs against rubrics to bring rigor and real‑world underwriting judgment to our AI training data.

This is a W-2 employment position with Cincinnatus LLC, with the opportunity to be placed at a leading AI Lab as part of their extended workforce.

2. Key Responsibilities

• Guide research and engineering teams to close knowledge gaps in underwriting, claims, and risk‑assessment reasoning.
• Design challenging, domain‑relevant insurance tasks and write accurate, well‑reasoned solutions grounded in real underwriting/claims practice.
• Evaluate AI model outputs against structured rubrics and provide clear, written feedback on correctness, judgment, and reasoning quality.
• Develop and refine evaluation guidelines and scoring rubrics specific to insurance tasks.
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data.

3. Core Qualifications

• 8+ years of dedicated professional experience in insurance (e.g., underwriting, claims, actuarial, risk management) at a recognized, top‑tier organization (e.g., AIG, Chubb, Allstate, Progressive, MetLife, Marsh McLennan, or equivalent).
• Prior hands‑on experience evaluating LLM/AI model outputs against rubrics or structured scoring criteria – mandatory; please describe this experience in your application.
• Demonstrable career progression (e.g., Underwriter → Senior Underwriter → VP of Underwriting).
• Ability to engage reliably for at least 35 hours/week during weekdays.
• Verbal and written communication skills, problem‑solving skills, and interpersonal skills.

Equal Employment Opportunity: Cincinnatus is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or any other legally protected characteristic.