Senior Commercial Underwriter, Underwriting Intelligence

Harper · San Francisco

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Senior Commercial Underwriter, Underwriting Intelligence

Harper is an AI-native commercial insurance company in San Francisco. We're not bolting AI onto insurance — we're rebuilding the entire business as software, on a simple bet: turning expert human judgment into compute is one of the largest transitions left to make, and a trillion-dollar industry still run 90% by hand is the place to prove it. We've grown ~100x in the last year and we move at that speed — on-site, in person, long days, very high standards. Almost no one joins Harper for insurance; they join to build the company that replaces how it works.

The role

A routing rule at Harper can move thousands of accounts. If the risk judgment behind it is vague, unsupported, or wrong, scale multiplies the mistake. Great underwriting judgment exists — it's just trapped in individual inboxes, one-off decisions, messy submissions, and tribal knowledge, most of all in specialty commercial and E&S, where risk moves through PDFs, ACORDs, loss runs, supplementals, and broker notes that may or may not answer what an underwriter needs to know. You make that judgment explicit, source-backed, and safe to apply at scale.

This is Harper's in-house underwriting encyclopedia, not an underwriting desk. You do not approve or decline individual accounts. You do not own market selection, the submission narrative, negotiation, or the commercial recommendation on any account; the licensed account owner keeps those. You own the risk judgment itself: the frameworks, the routing constraints, the appetite rules, the escalation conditions, and the source evidence behind each one. You don't need to write code. You do need to be the underwriter who sees a bad submission and thinks: what should intake have asked, what should the system have extracted, what rule would have caught this, what should we never send a carrier again.

Why now: Harper already has the data, live submissions, carrier outcomes, and systems that act at scale. The missing piece is senior judgment that can explain why one risk is strong, another is weak, and a third needs different information before anyone should make a call — and can write that down so a system can apply it and a person can audit it.

What you'll do

Who you are

The reality — read this before you apply

This job is on-site in New York or San Francisco, Monday through Friday, long days, in the building with product, engineering, intake, and the placement desk. You'll help build the system while it is already quoting live business, so there is context switching, ambiguity, and a steady stream of problems that don't fit a job description. You'll review live cases most days and you'll write more than most underwriters ever have: frameworks, rules, counterexamples, escalation conditions. Nothing is done until someone else can apply it and check it against the source.

This is not a traditional underwriting seat, and if that's what you want, don't apply. If you want authority to approve or decline individual accounts, if you want to own customer negotiation or placement strategy, if you prefer clean admitted risks and a fixed manual to ambiguous specialty work, or if you rely on unwritten instinct and don't want your reasoning made inspectable, this seat will frustrate you. Advising from a distance without getting into the submission, the system output, and the product logic doesn't work here, and neither does a remote or hybrid default. If the intensity reads as a cost you'd rather avoid, this isn't the seat. If it reads as the point, keep going.

Compensation & logistics

Benefits

Process

  1. Team call — 30 minutes on Zoom. You meet the team, we meet you.

  2. Office session — about two hours in person. You meet more of the team and work a real problem with us.

  3. Super day — how you actually operate, in real time. We decide after this.

The process moves as fast as you do.

To apply

Send your resume and one underwriting example: a risk where your first read changed after better information arrived — which facts mattered, which didn't, and how you would turn that judgment into a repeatable Harper framework.

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