LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings. We're expanding beyond lawn care to become the one-stop shop for all home services — operating across three brands (LawnStarter, Lawn Love, Home Gnome) on a single shared platform.
About Engineering at LawnStarter
We build in small, focused initiative teams: a Product Engineer working alongside a PM and a designer, supported by an Engineering Manager who helps you grow. You'll also work shoulder-to-shoulder with engineering peers across initiatives in a shared codebase. AI coding agents are a force multiplier here — they give a small, senior team the leverage to ship more, faster, and at a higher bar for quality. We hire engineers who are wired for ownership and energized by shipping to a real marketplace with customers and pros on both sides.
When an agent can ship something safely, your job is to make sure it's done right and the metric moves. When the work touches a sensitive area, you have the guardrails to prove the change does exactly what it should, with no side effects: strong tests, evals, staged rollouts, and review gates.
You sit at the table with PM and design, bringing engineering judgment to product calls and product sense to engineering calls.
The technical approach — architecture, data model, integration choices, rollout plan, observability, and rollback strategy for your initiative. you document them, and revisit if the data says you were wrong.
Implementation quality — the prompts, guardrails, evals, tests, and review loop that let agents ship safe, correct, production-ready code. Cross-functional partnership — daily working contact with your PM (scope, tradeoffs) and designer (UX decisions, in-tool prototyping), regular collaboration with engineering peers, and weekly check-ins with your EM.
With your PM, you present results 2–4 weeks post-launch and share the "did it work" answer.
A high bar for what ships — production correctness, security, performance, observability, and the experience for customers and pros. Leading AI agents at a staff-level quality bar Most of the code on your initiative will be authored by AI agents. The craft is making them ship as if a senior engineer wrote it: prompts that encode our conventions, evals that catch issues before merge, tests that exercise the edges, observability that catches a regression before a customer does. Owning decisions with high autonomy You have real latitude to make and document technical calls quickly — with architect review on the big architectural ones and peers to pressure-test your thinking. Shipping outcomes, not features Each initiative is measured by a metric — a conversion rate, a retention curve, a pro-funnel KPI, a unit-economics shift. How do you scope to actually move it, decide what *not* to build, and have the discipline to follow up 2–4 weeks after launch?
Initiative outcomes hit — You've shipped 3–4 initiatives end-to-end, and at least two clearly moved their metric (with the post-launch review to prove it).
Quality holds — No customer- or pro-facing regression traceable to agent-authored code that slipped through your review.
Whatever your current title, you've been the person making the call, shipping the hard thing, and standing behind whether it worked.
You bring engineering judgment to product calls and product judgment to engineering calls.
You make architecture, data-model, and rollout calls, write them down, get fast input, and move.
People management sits with the EM — but the path into management is an open door for those who want it.
A product-engineering role, end-to-end. Hands-on, with a high quality bar. AI agents — Claude Code, Cursor, Codex, internal agent stack, MCP servers, evals tooling
Backend — PHP/Laravel
Frontend — TypeScript/React/React Native (customer & pro apps, web and mobile)
Data — Redshift, dbt, Segment, Airflow
Infra — AWS, Datadog, Sentry, GitHub Actions
Documentation & process — Brain (Claude Code skills + docs repo), Confluence, Jira
You need deep skill in at least one of our stacks plus credible production experience with AI coding agents.

📌 Staff Software Engineer, Frontend Platform, Remote (Ciudad de México)
🏢 Lawnstarter
📍 Ciudad de México