LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, and they are seeking a Senior Product Manager for Service Delivery. This role focuses on improving the post-booking experience by managing the expectations and interests of both customers and service providers, leveraging AI to enhance support and communication.
Responsibilities:
- Setting the right expectations on both sides of the marketplace
- Steering Pros to deliver great work
- Resolving conflicting interests fairly
- Making AI-powered support genuinely good
- Shaping strategy, policy, and the systems behind the post-booking experience
- Building policies and decision systems that resolve conflicts fairly and consistently at scale
- Owning the eval systems for AI support and ensuring quality
- Improving messaging reliability and engagement
Requirements:
- AI-native. You use AI daily in your own work, and you have real intuition for how to measure whether an AI experience is actually good — not just whether it shipped. You've built or owned evals, or you're hungry to, because you know that's what separates a trustworthy agent from a demo. This is unlikely to be a good fit if you treat LLM quality as a vibe check or as engineering's problem to figure out
- Comfortable making two-sided calls. You can hold both the customer's and the Pro's interest in your head at once, and you're willing to make the call when they conflict — clearly, and with a rationale you'd defend to either side. This is unlikely to be a good fit if you're a people-pleaser who can't say no, or if you instinctively optimize for one side and forget the other exists
- You design for the right outcome up front. You're not satisfied grading work after the fact — you'd rather get the expectations right at the start so the job goes well in the first place, for both the customer and the Pro. This is unlikely to be a good fit if you gravitate to measuring and auditing results over shaping them before they happen
- A marketplace systems thinker. You see service delivery as a system of incentives, policies, and feedback loops — not a set of screens — and you design rules that hold up across edge cases and bad actors. This is unlikely to be a good fit if your instinct is to solve every problem with UI rather than incentives and policy
- Data-informed. You live in the numbers that matter here — CSAT, resolution rate, eval scores, churn, deflection — and you know when the data is thin enough that a judgment call is needed. This is unlikely to be a good fit if you either ignore data or refuse to move without perfect information
- Technically fluent. You partner with engineers on how AI and messaging systems work and give real feedback on design tradeoffs. You don't write production code, but you don't treat the systems as a black box either. This is unlikely to be a good fit if you need everything translated out of technical terms first