Product Manager

Harper•San Francisco, CA

1d•$125,000 - $170,000•Onsite

About The Position

Harper is building an AI-led insurance brokerage, aiming to serve the 36 million businesses in America that are currently underinsured due to a slow, opaque, and confusing distribution system. Over 90% of commercial insurance is human-led, but Harper is building AI to make humans more effective, improve customer experience, and eliminate friction. The company has experienced 100x growth since last year and is adding approximately 1,000 customers per month. This role involves owning a module within the company's operational factory, encoding nuance into AI, and driving key metrics. The company's thesis is that industries with human-bounded distribution consolidate rapidly once they become computational, leading to expansion as efficiency increases. Harper aims to be an AI-native company where knowledge is encoded into systems queryable by agents and operators, creating a competitive moat. Unlike AI tools sold to brokers, Harper acts as the broker, handling the entire process end-to-end to provide small businesses with the right coverage quickly and affordably.

Requirements

1–3 years into product, OR an early-career operator, engineer, or AI researcher who's been doing the work without the title
Demonstrated ownership of a product or system end-to-end—KPIs, roadmap, execution
Proficiency using AI tools to prototype (Claude Code, Cursor, Lovable, or similar)
Strong analytical instincts—you can argue tradeoffs with engineers on AI systems
Track record of going deep on a domain and encoding what you learned into a system
Based in San Francisco or willing to relocate

Nice To Haves

Background in AI/ML products, voice AI, agent frameworks, or workflow automation
Experience with eval design, prompt engineering, or context engineering
Insurance, fintech, or regulated industry experience
Prior startup experience

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.
Encode the nuance — Every module has industry-by-industry, segment-by-segment behavior to encode. You 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 humans trust them. Regressions on every change, evals that map 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 so we can train, evaluate, and improve against a real bar.
Own the cross-modal experience — Your module spans web, voice, and human. You decide where each modality wins, where they hand off, and 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 things people are asking your module to do, find the 3 that move the KPI and ignore the rest with conviction.