Learning & Knowledge Systems Lead

Responsibilities

• Two tracks, running in parallel, at the intersection of Operations, Engineering, and RevOps.
• Build the operating memory.
• Embed with sales, intake, service, placements, and renewals. Sit with operators, listen to calls, shadow workflows, and document what people "just know."
• Turn transcripts, Slack threads, Looms, and one-off explanations into source-of-truth docs, decision logs, playbooks, process maps, glossaries, and system-boundary docs (what each internal system does, where one stops and the next begins, and what gets misread).
• Graduate stabilized rules into skills the agents can call , and partner with engineering on refresh automations so docs stay alive instead of going stale.
• Hunt the edge cases - reworks, escalations, stale quotes, market follow-ups, binder/payment gaps, customer confusion - and write them down before they bite again.
• Get the company to run against it.
• When a rich AI-generated playbook lands, distill it into an executable plan: named owners, the first three moves, a rollout cadence and date. The plan doesn't run itself; you make it run.
• Build the onboarding paths and setup scripts that get a new hire into Cursor, Claude Code, and the harness within a week.
• Run cohort rollouts, drive adoption, and make activity visible - we should know who's actually in the harness.
• Shape meetings in real time so they produce useful artifacts: decisions, owners, definitions, edge cases, open questions, next steps.
• When the same problem shows up three times, turn it into a playbook, a QA check, a skill, or a product requirement. You'll work directly with the CEO when extraction calls for it.

Requirements

• An exceptional writer and synthesizer. You can take a messy transcript to a clear operating doc, and a clear doc to something a team actually executes against.
• Genuinely curious about how organizations work, and happy to sit with operators to find out.
• Structured but not bureaucratic. You care whether documentation changes behavior, not whether it looks polished.
• Able to operate in chaos without becoming chaotic. Low-ego, persistent, and allergic to "someone should probably document that."

Additional Information

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.

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