Harper
Product Manager
San Francisco · Junior
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About the role
- Harper isn't an AI tool sold to brokers.
- We are the broker - we do the work end-to-end and sell the outcome: the right coverage, fast, at the right price, with the right service.
- Owning both sides is the moat, and the companies that win this transition won't just have great AI; they'll have figured out how to organize themselves around it, so that knowledge gets encoded into systems agents and operators can query.
Responsibilities
- Own the KPIs. Conversion, handle time, accuracy, autonomous-resolution rate, retention - whatever the leverage point is for your surface. You set the targets, instrument them, move them. If the metric isn't moving, that's your problem.
- Own the eval regime.
- Build the data flywheel. Work hand-in-glove with data labeling and validation to build the golden datasets your module's models need. You define what "right" looks like.
- Own the cross-modal experience. Your module spans web, voice, and human. You decide where each modality wins, where they hand off, how the on-ramps feel.
- If you want to own a module that touches real customers from week one - send your resume and tell us about something you built that moved a number.
Requirements
- 1-3 years in product, or an early-career operator, engineer, or AI researcher who's been doing the work without the title.
- Demonstrated end-to-end ownership of a product or system - KPIs, roadmap, execution - and a track record of going deep on a domain and encoding what you learned into a system.
- You can argue AI tradeoffs (agents, LLMs, context engineering, data pipelines, evals) with the engineer who writes the code, even though you don't write it.
Nice to have
- eval/prompt/context engineering
- insurance, fintech, or regulated-industry experience
- prior startup experience.
Compensation
- $125,000-$170,000 base + performance bonus + equity.
Benefits
- Uber commuter benefits; breakfast, lunch, and dinner provided; snacks and coffee stocked; free gym membership; health, dental, and vision.
Company info
- Find what's broken before they tell you. - Talk to customers every day.
- How a daycare buys insurance versus a trucking company.
- Encode the nuance. Translate what makes your module's customers different into rules, prompts, agents, and data structures.
- Talk to customers every day. Literally - not "5 calls last quarter."
- You get what an AI services company is: we're not selling software, we're doing the work and selling the outcome, which means you ship behavior into a probabilistic system real operators and customers have to trust.
This listing is sourced directly from Harper's careers page and normalized into a canonical job model.