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OPERATING MEMORY 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 The bet — turning human judgment into compute — has a precondition: the judgment has to be written down. AI doesn't magically understand a company. It works only when the business is documented clearly enough for systems to retrieve the right context, recognize the workflow, handle the edge cases, and escalate when a human is actually needed. Right now most of Harper's operating knowledge lives in people's heads: how a top rep prioritizes quotes, how service handles an edge case, which underwriter to chase, what a customer really means when they push back at bind, why a workflow changed yesterday. That works at small scale and breaks at ~1,000 new customers a month. The next bottleneck here isn't engineering — it's knowledge. Every process that lives only in someone's head is a future failure mode. Every undocumented edge case is rework. A workflow that isn't clear enough for a new hire isn't clear enough for an AI agent either. You turn that messy operating reality into structured, AI-legible knowledge — and make sure Harper's knowledge compounds instead of disappearing. WHAT YOU'LL DO - Capture tribal knowledge. Embed with sales, intake, service, placements, and renewals. Sit with operators, shadow workflows, listen to calls, read transcripts, and document what people "just know." - Build operating memory. Turn transcripts, Slack threads, Looms, and one-off explanations into source-of-truth docs, decision logs, playbooks, process maps, onboarding paths, and glossaries. - Use AI as a force multiplier. Build repeatable workflows that turn raw context into decisions, owners, open loops, SOPs, training material, and product requirements. - Make meetings AI-legible. Shape conversations in real time so they produce useful artifacts — decisions, owners, definitions, edge cases, unresolved questions, next steps. Ask the questions that turn a meeting into an artifact: who owns this, what's the exception, what's the source of truth, what would someone misread from the transcript alone. - Find the edge cases. Document where workflows break — reworks, escalations, stale quotes, underwriter follow-ups, payment/binder gaps, COI delays, customer confusion. - Translate ops into product. Sit between operators and engineering; capture what people do, where tools fail, what workarounds exist, what needs to be built. - Maintain the knowledge base. Keep docs current, assign owners, kill stale guidance, make sure people know where the truth lives. - Turn repeated problems into systems. If the same issue happens three times, it becomes a playbook, a QA check, a training artifact, or a product requirement. WHO YOU ARE - An exceptional writer and synthesizer who can turn a messy transcript into a clear operating doc the same day. - Genuinely AI-native in practice — not "I use ChatGPT," but you have taste for when an output is structurally wrong, not just stylistically off. You prompt for extraction (decisions, contradictions, owners, edge cases), not just summarization, and you know good output depends on good context you engineer upstream. - Curious about how organizations actually work, and you like sitting with operators. - You notice hidden assumptions, missing ownership, and contradictions other people walk past. - Structured but not bureaucratic; you care whether documentation changes behavio