Product Manager

Product Manager

Harper is an AI-native commercial insurance company in San Francisco.

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. We've grown ~100x in the last year and we move at that speed — on-site, in person, long days, very high standards. Almost no one joins Harper for insurance

; they join to build the company that replaces how it works.

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. That's the question this role sits inside.

You'll own a module of the business end-to-end — the customer experience, the operator workflows, and the AI agents underneath — and run it with a forward-deployed engineer and the operators who live in it: sales, service, underwriting, ops. The founding PM team has gone wide across every module; now we go deep. How a daycare buys insurance versus a trucking company. What "urgency" means for a tow yard with expiring dealer plates versus a GL renewal.

Your job is to encode that nuance into the systems until they do the work as well as a human in most places and better than a human in many. Own the module, move the metric, then go own the next.

What you'll do

• 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.
• Encode the nuance.
• Translate what makes your module's customers different into rules, prompts, agents, and data structures.
• Own the eval regime.
• Probabilistic systems are only valuable when people trust them: regressions on every change, evals mapped to real outcomes (not vibes), backtests against historical applications, call-by-call review where it matters. You'll be paranoid about silent regressions in a way most PMs aren't.
• 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.
• Live with operators.
• Sit with sales, service, underwriting. Watch the work. Find what's broken before they tell you.
• Talk to customers every day.
• Literally — not "5 calls last quarter."
• Prototype with AI.
• Claude Code, Cursor, Lovable. Walk into the meeting with a working prototype, not a deck.
• Hyper-prioritize.
• Out of 50 asks, find the 3 that move the KPI and ignore the rest with conviction.

What we're looking for

• 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 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.
• 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").
• You can build: Cursor, Claude Code, Lovable. 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.
• You go deep before you go wide, and you want to own a thing, not coordinate one. If "PM" sounds like meetings, this isn't it.
• Bonus: AI/ML products, voice AI, agent frameworks, or workflow automation; eval/prompt/context engineering; insurance, fintech, or regulated-industry experience; prior startup experience.
• The reality
• On-site in San Francisco, Monday–Friday, roughly 5 AM–8 PM. 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. The people who thrive here wouldn't have it any other way. 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.

Logistics

Location:

San Francisco, in-office. Based here or willing to relocate.