Commercial Underwriting Advisor

Harper
San Francisco$150k – $190kPosted 24 February 2026

Tech Stack

Job Description

The Problem36 million businesses in America need insurance—it's not optional. 77% are underinsured. 40% have no coverage at all. The distribution system failed them: too slow, too opaque, too confusing.Over 90% of commercial insurance is still human-led. We're building the inverse: 90%+ AI-led, pushing toward the higher 90s. Not by patching legacy workflows—by building AI that makes humans more effective, improves the customer experience, and eliminates friction at every step.We're adding ~1,000 customers per month. We've grown 100x since last year. We're looking to do even more this year—and that's why we're hiring.You're the underwriting lens that makes sure everything we build actually holds up in the real world.The ThesisYou'll pull up real submissions and AI outputs on day one. You'll review what our system generated, compare it to what a carrier underwriter would actually expect, and tell us—directly—what's wrong. You've done this your entire career. The difference here is that your judgment doesn't get applied to 50 deals. It gets scaled across thousands.Harper has its own operating system and AI infrastructure. We're not looking for someone to recommend vendor tools or bring in external processes. We need someone who's lived the carrier side—who knows what makes a submission stand out vs. get kicked back—and wants to shape our systems from the inside. You're not the AI. You're the person who makes sure the AI thinks like an underwriter.The RoleYou won't manage a book of business, bind policies, or own carrier relationships. But this isn't less work than a traditional underwriting seat—it's different work, and it's harder. Your focus is on product and process—making sure our systems work the way the industry actually works, every day, in the details.You'll sit next to engineers and shape how our AI thinks about risk. You're a thought partner, not a requestor who submits feedback and waits. When something's wrong with a submission workflow, you don't write a memo—you walk over and fix it together. There is no established underwriting review function waiting for you. No QA team, no structured review process. You're building this while doing the work.This is a small team, high-intensity environment. The CEO is in the details on submissions, quoting speed, and carrier relationships. Everyone operates well beyond their job description. If that sounds like where you do your best work, keep reading.What You'll DoReview real submissions and AI outputs daily — Pull up what our system generated, compare it to what a carrier underwriter would expect, and tell us what's broken; this is your core loop, not a periodic reviewBuild the underwriting review function from scratch — There's no established QA process for submission quality; you define what good looks like, create the review cadence, and own the standardGate what ships — Review risk assessment logic, submission workflows, and quoting outputs before they reach customers; nothing with underwriting implications goes live without your sign-offShape how our AI thinks about risk — Work directly with engineers to translate underwriting judgment into system logic; you're a thought partner sitting next to the team, not a requestor sending requirements over emailGive us carrier-side feedback — Tell us what carriers and wholesale partners actually care about; what frustrates them about working with brokerages; help us build processes that make their lives easierFlag what we're missing — Coverage gaps, compliance issues, edge cases, and submission quality problems that a carrier-side underwriter would catch immediately but our team might missYou Might Be a Fit If...Your instinct is to pull up the actual submissions before reading any strategy docsYou've spent 7+ years in commercial P&C underwriting and you know the submission-to-bind lifecycle coldYou'd rather shape an internal AI system than recommend buying a vendor solutionYou're comfortable giving direct, unfiltered feedback and pok ... (truncated, view full listing at source)
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