Learning&KnowledgeSystemsLead

LEARNING & KNOWLEDGE SYSTEMS LEAD 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 IN ONE LINE You turn the judgment locked inside Harper's best operators into AI-legible knowledge — living docs, decision logs, and retrievable skills the agents can actually call — and you get the rest of the company running against it. WHY THIS ROLE EXISTS NOW AI doesn't understand a company by default. It works only when the business is documented clearly enough for a system to retrieve the right context, recognize the workflow, handle the edge case, and escalate when human judgment is required. Right now most of how Harper operates lives in people's heads: how a top rep sequences quotes, how service handles the weird bind, how market routing actually works, what a customer means when they push back. That holds at small scale. It breaks at ~1,000 new customers a month. Every undocumented process is a future failure mode; every AI-generated playbook that dies in a chat thread is throughput left on the floor. The next bottleneck here isn't engineering. It's knowledge — and how fast people can absorb it. This role removes that bottleneck. Be clear about what this is not. This is not corporate L&D. No LMS, no slide decks, no e-learning project, no making-the-Notion-pretty. This is knowledge engineering: sit with operators, extract how they actually think, and turn it into structured knowledge a human and a model can use. WHAT YOU'LL DO Two tracks, running in parallel, at the