Operating Memory Lead

The Problem 36 million businesses in America need insurance—it's not optional. 77% are underinsured. 40% have no coverage at all. The distribution system failed them: too slow, too opaque, too confusing. Over 90% of commercial insurance is still human-led. We're building the inverse: 90%+ AI-led, pushing toward the higher 90s. But to do that, everything we do has to become legible. Today, most of Harper's operating knowledge lives in people's heads—how a top rep prioritizes quotes, how service handles edge cases, 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. It breaks at ~1,000 new customers a month. We need someone to turn tribal knowledge into operating memory. The Thesis AI doesn't magically understand a company. It only works when the business is documented clearly enough for systems to retrieve the right context, recognize the workflow, handle the edge cases, and elevate when human judgment is needed. The next bottleneck at Harper is not engineering. It's knowledge. Every process that lives only in someone's head is a future failure mode. Every undocumented edge case is another rework. Every meeting decision that disappears is a repeated argument. Every workflow that's not clear enough for a new hire is not clear enough for an AI agent either. You’ll turn messy operating reality into structured, AI‑legible knowledge—and make sure Harper's knowledge compounds instead of disappearing. The Role You embed with teams across sales, intake, service, placements, and renewals. You sit with operators while they work. You listen to calls, read transcripts, study workflows, and figure out what's actually happening underneath the chaos. Then you turn that into operating memory: source‑of‑truth docs, SOPs, playbooks, decision logs, process maps, onboarding paths, glossaries, and AI‑readable knowledge bases. You don't need to be an engineer. You do need to be exceptional at using AI tools—Granola, Claude, ChatGPT, transcript workflows, structured prompting, deep research, AI‑assisted synthesis—to turn raw context into reusable work product. A big part of the job is leaving breadcrumbs for AI. Shaping conversations so transcripts are useful later. Asking the questions in real time that turn a meeting into an artifact: who owns this, what's the exception, what's the source of truth, what would someone misunderstand from the transcript alone, is this a temporary workaround or the new process. This is not a note‑taking role. This is not "make the Notion pretty." This is an operating role for someone who can walk into ambiguity, find the hidden logic, and turn it into systems. What You’ll Do Capture tribal knowledge — Interview operators, shadow workflows, sit with teams, 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, and process maps 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 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 are doing, 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 You Might Be a Fit If… You're an exceptional writer and synthesizer You're curious about how organizations actually work, and you like sitting with operators You can turn a messy transcript into a clear operating doc the same day You're AI‑native in practice—not just "I use ChatGPT," but you have taste for when an output is structurally wrong, not just stylistically off You know how to prompt for extraction (decisions, contradictions, owners, edge cases) not just summarization You understand that good AI output depends on good context, and you know how to engineer context upstream You notice hidden assumptions, missing ownership, and contradictions other people walk past You're structured but not bureaucratic You care whether documentation changes behavior, not whether it looks polished You can operate in chaos without becoming chaotic You're low‑ego, persistent, and allergic to "someone should probably document that" Backgrounds That Could Work We're open‑minded. Strong candidates might come from qualitative or academic research, ethnography, instructional or curriculum design, knowledge management, product ops, technical writing, research ops, implementation, chief of staff roles, library and information science, AI ops / human‑in‑the‑loop work, or startup operations roles where the systems were messy and someone had to make them legible. 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. 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 Ability to turn messy conversations into clear decisions, workflows, and source‑of‑truth documentation Strong information‑architecture instincts Comfort in a fast‑moving, ambiguous startup Based in San Francisco or willing to relocate Nice to Have Experience at a high‑growth startup Background in qualitative research, ethnography, curriculum design, or library/information science Experience working with sales, service, customer success, or operations teams Experience with RAG/search systems, data labeling, human‑in‑the‑loop QA, or internal automation Experience translating operator feedback into product requirements Experience building internal knowledge bases in Notion, Confluence, Guru, Coda, or similar Experience with taxonomy, metadata, tagging, or content governance Experience in insurance, fintech, B2B services, or another high‑volume operational environment Compensation Salary: $90,000–$150,000 + performance bonuses & equity Location: San Francisco, in‑office Schedule: Monday–Friday, in‑office hours that match the rest of the company. The hours are long. The people who thrive here wouldn't have it any other way. Benefits Health, dental, and vision insurance Commuter benefits Team meals and snacks High ownership, high visibility, and direct exposure to how an AI‑native company is built The Process 15‑minute founder call — Alignment on mission, pace, and role fit Work sample — Turn messy source material into structured operating memory using AI tools On‑site super day — Sit with operators, review transcripts, meet product and engineering, show how you think 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 you built—and show how you used AI tools to do it faster or better. #J-18808-Ljbffr