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Harperinsure

Operating Memory Lead

Salary$90k – $150k
LocationSan Francisco
Work modeon-site
Typefull-time
DepartmentOperations
EquityIncluded
Company size11+ people
First seen3mo ago
Last seen1d ago
Operating Memory Lead
About the company
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
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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.”
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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.
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Use AI as a force multiplier. Build repeatable workflows that turn raw context into decisions, owners, open loops, SOPs, training material, and product requirements.
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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.
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Find the edge cases. Document where workflows break — reworks, escalations, stale quotes, underwriter follow-ups, payment/binder gaps, COI delays, customer confusion.
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Translate ops into product. Sit between operators and engineering; capture what people do, where tools fail, what workarounds exist, what needs to be built.
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Maintain the knowledge base. Keep docs current, assign owners, kill stale guidance, make sure people know where the truth lives.
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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
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An exceptional writer and synthesizer who can turn a messy transcript into a clear operating doc the same day.
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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.
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Curious about how organizations actually work, and you like sitting with operators.
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You notice hidden assumptions, missing ownership, and contradictions other people walk past.
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Structured but not bureaucratic; you care whether documentation changes behavior, not whether it looks polished.
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Low-ego, persistent, and allergic to “someone should probably document that.”
Requirements: 2–8 years in research, product ops, knowledge management, technical writing, implementation, chief-of-staff work, qualitative research, instructional design, or startup operations; exceptional written communication; strong AI-tool fluency (Claude, ChatGPT, Granola, transcript workflows, structured prompting, AI-assisted synthesis); demonstrated ability to interview stakeholders and extract operational detail; strong information-architecture instincts; comfort in a fast-moving, ambiguous startup; based in SF or willing to relocate.
Nice to have
Backgrounds that could work: qualitative or academic research, ethnography, instructional/curriculum design, knowledge management, product ops, technical writing, research ops, implementation, chief of staff, library and information science, AI ops / human-in-the-loop work, or messy startup operations. The exact background matters less than the ability to extract knowledge, impose useful structure, use AI tools well, and create artifacts that change how people work.
The honest day-to-day
So the right person applies and the wrong person doesn’t, plainly:
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This is not a note-taking role, and it is not “make the Notion pretty.” If your mental model is tidy documentation, this isn’t it. This is an operating role: walk into ambiguity, find the hidden logic, and turn it into systems people and AI actually use.
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Real AI-tool fluency is a hard gate, not a nice-to-have. You’ll be judged on whether your AI-built artifacts are structurally correct and reusable — taste matters more than tool familiarity.
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You succeed only if behavior changes. A beautiful doc nobody uses is a failure here. The measure is whether the workflow got faster, the edge case stopped recurring, the new hire ramped without a meeting.
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On-site SF, long days. Mon–Fri, in-office hours that match the rest of the company.
Compensation & logistics
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Salary: $90,000–$150,000 + performance bonuses & equity
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Location: San Francisco, in-office. Based in SF or willing to relocate.
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Schedule: Monday–Friday, in-office hours matching the rest of the company.
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Benefits: Uber commuter benefits; breakfast, lunch, and dinner provided; snacks, drinks, and coffee daily; free gym membership; health, dental, and vision insurance.
Process
1.
15-minute founder call — alignment on mission, pace, and role fit
2.
Work sample — turn messy source material into structured operating memory using AI tools
3.
On-site super day — sit with operators, review transcripts, meet product and engineering, show how you think
4.
Final conversation — scope, comp, start date
To apply
Send your resume and tell us about a time you turned messy, undocumented knowledge into something other people actually used. Bonus points if you include the artifact — doc, playbook, process map, onboarding guide, research synthesis, curriculum, or internal system — and show how you used AI tools to do it faster or better.
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