Harper
Learning & Knowledge Systems Lead
San Francisco · Mid · Part-time
Sponsorship not specified$110k-$170kDetected 143 days ago
RESTRAGCadenceResearchCommunicationWriting
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odds of building a lasting career here
54Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level III)83
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~45% per draw at Level III). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
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Files H-1B transfers
4 transfer filings in the last year, covering 4 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
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About the role
- 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.
- AI doesn't understand a company by default.
- 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.
Responsibilities
- 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."
- Graduate stabilized rules into skills the agents can call, and partner with engineering on refresh automations so docs stay alive instead of going stale.
- 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.
- Almost no one joins Harper for insurance; they join to build the company that replaces how it works.
- Able to operate in chaos without becoming chaotic. Low-ego, persistent, and allergic to "someone should probably document that."
Requirements
- 3-8 years in a relevant field (see above).
- Demonstrated ability to interview stakeholders, extract operational detail, and turn messy conversations into clear decisions and source-of-truth docs.
- A track record of running an adoption rollout that actually changed how a team worked.
- Comfort in a fast-moving, ambiguous startup.
- You can take a messy transcript to a clear operating doc, and a clear doc to something a team actually executes against.
- Not "I use ChatGPT." You have taste for when an output is structurally wrong, not just stylistically off.
- You know good output depends on good context, and you engineer that context upstream.
Nice to have
- experience at an AI-native or dev-tools company
- authoring Claude/agent skills or similar capability modules
- RAG/search systems, data labeling, evals on knowledge systems, or human-in-the-loop QA
- working with engineering on living-doc refresh automations
- translating operator feedback into product requirements
- taxonomy/metadata/content governance
- insurance, fintech, B2B services, or another high-volume operational environment.
- The hours are long and the standards are high, because a rebuild this large doesn't happen part-time.
Compensation
- Salary: $110,000-$170,000 + performance bonuses & equity
Benefits
- $110,000-$170,000 + performance bonuses & equity
- Uber commuter benefits; breakfast, lunch, and dinner provided; snacks/drinks/coffee daily; free gym membership; health, dental, and vision insurance.
Company info
- It breaks at ~1,000 new customers a month.
- 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.
This listing is sourced directly from Harper's careers page and normalized into a canonical job model.