
Harper
Account Executive, New Business
San Francisco
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odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
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About the role
- AI now does most of the work of getting a business quoted and covered at Harper - but a human still closes, and that human is you.
- You sit at the exact line where the machine hands off to judgment:
- you take warmed, qualified small-business owners and turn them into customers, on the phone, at volume, all day.
Responsibilities
- Almost no one joins Harper for insurance; they join to build the company that replaces how it works.
- Lead screen - fit and alignment
Requirements
- You're a closer first, with a real track record of closing at high velocity and high volume.
- You treat AI as leverage - using it to prep and qualify faster - and you have opinions about where it helps and where it gets in the way.
- You'll need a commercial insurance producer license within roughly your first 30 days - we pay for the training and the exam, but you have to pass it, fast, while you're also ramping on the phones.
- Licensing: Commercial insurance producer license required within ~30 days of start
- Commercial insurance producer license required within ~30 days of start; Harper pays for training and licensing.
Compensation
- $150,000-$200,000 OTE.
Benefits
- Uber commuter benefits
- Free gym membership
- Health, dental, and vision insurance
Company info
- You sit at the exact line where the machine hands off to judgment: you take warmed, qualified small-business owners and turn them into customers, on the phone, at volume, all day.
- 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.