Harper
Operating Memory Lead
San Francisco · Mid
Sponsorship not specified$90k-$150kDetected 147 days ago
RESTLogisticsResearchCommunicationWritingUnderwriting
Stay score
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.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
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.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About 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.
Responsibilities
- 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.
- 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.
- Maintain the knowledge base. Keep docs current, assign owners, kill stale guidance, make sure people know where the truth lives.
- Sit with operators, shadow workflows, listen to calls, read transcripts, and document what people "just know."
- Almost no one joins Harper for insurance; they join to build the company that replaces how it works.
- Low-ego, persistent, and allergic to "someone should probably document that."
Requirements
- demonstrated ability to interview stakeholders and extract operational detail
- comfort in a fast-moving, ambiguous startup
- 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.
Compensation
- $90,000-$150,000 + performance bonuses & equity
Benefits
- Uber commuter benefits; breakfast, lunch, and dinner provided; snacks, drinks, and coffee daily; free gym membership; health, dental, and vision insurance.
- 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.
Company info
- 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.
- 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.
- On-site SF, long days. Mon-Fri, in-office hours that match the rest of the company.
- That works at small scale and 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.