Principal Product Manager

🏢 LawnStarter · all 9 jobs
📍 Austin, Texas, United States
📅 Posted Sep 4, 2026 · via WorkableBoard
🏷 Remote
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About LawnStarter

LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in bookings. We're expanding beyond lawn care to become the one-stop shop for home services: three brands (LawnStarter, Lawn Love, Home Gnome), one shared platform.

About Product at LawnStarter

Our BHAG: 1+ job per household, every year. Getting there means winning on four fronts at once: more customers, more Pros, excellent service experience for both sides, and an AI-run company that moves faster than its size should allow. We've organized around those bets as swim lanes: New Customer Acquisition, Strengthen Supply, Service Experience, and AI & Operating Excellence.

The Role

This is the most senior individual contributor seat in Product, with no direct reports. You'll work as a peer to the leaders of Ops, Data, Design, and Engineering.

Senior Product Managers here each own a workstream inside one of four swim lanes. You drive the Service Experience product agenda end to end: everything after a customer books, for customers and Pros, and the AI increasingly behind it. Two jobs at once: a critical thought partner to leadership on how AI reshapes our service experience, and an operator who ships with peers in engineering, design, data, marketing, and ops at a speed and quality that most would not believe. You're accountable for company-level numbers, not initiative-level ones.

What makes this role different:
- Thought partner, not order-taker.  Leadership wants your view on where AI takes the service experience, and expects you to change their minds sometimes.
- Speed and quality together.  Small team, peers in engineering, design, data, marketing, and ops who move fast, and few layers between your judgment and what ships.
- Ultra AI-forward is the point.  We want someone already operating the way most PMs will operate in five years.
- Strategy and hands.  You still prototype with LLMs, write eval sets, read transcripts, and re-architect live systems yourself.

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