Harper

Product Manager

San Francisco

Sponsorship not specified$125k-$170kDetected 56 days ago

RESTMachine LearningData EngineeringLLMsAgentic AILogisticsUnderwriting

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.

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.
If that's a cost you're glad to pay, this is one of the few PM seats where you own a real piece of the business from week one.

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.

Uber commuter benefits; breakfast, lunch, and dinner provided; snacks and coffee stocked; free gym membership; health, dental, and vision.
The hours are long and the learning curve is steep - we hire early-career PMs on purpose and hand them the kind of surface area and reps that take a decade somewhere else.

You're obsessed with evals - you'd rather ship a worse model with a great eval harness than the reverse - and you think in KPIs ("we cut handle time 40%," not "we shipped the feature").
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.