Principal Underwriter

Sixfold Bioscience
New York, NY$125k – $150kPosted 26 March 2026

Tech Stack

Job Description

Who we are At Sixfold, we’re building the AI platform that transforms how insurers evaluate and price risk. Not just another tool that generates summaries or flags issues—we’re creating AI agents that actually understand risk the way veteran underwriters do. Our platform is processing billions in premium for some of the world’s largest carriers, and we’re just getting started. The technical challenges are wild. We’re teaching AI to understand that a bakery in Florida faces different risks than one in Montana. To know when a manufacturing company’s pivot from toys to medical devices fundamentally changes their risk profile. To make million-dollar decisions with the same intuition as someone who’s been underwriting for 20 years. What makes Sixfold special isn’t just the technology—it’s that we’re building it with people who deeply understand insurance. Our team includes folks who’ve built and scaled carriers, researchers who’ve pushed the boundaries of AI, and engineers who just love solving seemingly impossible problems. We’re still early, but the impact is already real. If you want to build AI that matters—that affects real businesses, real people, and billions in economic activity—Sixfold is where you should be. We’re not just digitizing insurance. We’re reimagining what it can be. The Role We are hiring a Principal Underwriter, AI Strategy Systems to serve as the domain expert behind our AI underwriting platform. This is not a traditional underwriting desk. You are the architect of how our AI thinks about risk. As a Principal, you will own the intellectual framework that shapes our platform's underwriting judgment. You will define what "good" looks like across commercial lines, build the systems that measure it, and create the ground truth that engineers and product teams use to train, evaluate, and improve our models. You will work directly alongside AI engineers and product teams to translate decades of underwriting judgment into structured, scalable knowledge, and you will own the feedback loop when the system gets it wrong. What You Will Do Evaluation Framework Design Own the design of evaluation criteria that measure underwriting competency across specific dimensions (risk selection, coverage analysis, pricing adequacy, hazard identification, and loss history interpretation) Define what "correct" looks like for AI-generated underwriting output and establish the scoring methodology to measure it Develop evaluation frameworks aligned with professional underwriting competency standards and carrier best practices Knowledge Base Development, Validation AI Benchmarking Serve as the primary architect and validator of our underwriting knowledge base across commercial PC lines (Commercial Property, General Liability, Workers Comp, Commercial Auto, Umbrella, Professional Liability, Inland Marine, Cyber Liability) Author and own underwriting guidelines, risk appetite frameworks, and line-of-business knowledge documents that define how the platform thinks Identify gaps, errors, and ambiguities in AI-generated underwriting analysis and provide expert corrections with clear, structured rationale Ensure knowledge base content reflects current market practices, regulatory requirements, and carrier appetite standards Annotate commercial insurance submissions, loss runs, schedules of values, and supplemental applications with expert underwriting judgment Develop and own annotation guidelines that capture how experienced underwriters evaluate risk across lines of business and industries Build and maintain gold-standard evaluation datasets that serve as ground truth for AI model benchmarking and improvement Who You Are Required 10+ years of commercial lines underwriting experience across multiple lines of business (minimum 3 of the following: Property, GL, WC, Commercial Auto, Umbrella, Professional Liability, Inland Marine, Cyber) Demonstrated multi-industry experience underwriting accounts across diverse NAICS cla ... (truncated, view full listing at source)
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