Job Opportunities API

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 (a