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Salary: USD 85,000 - 125,000 / annual
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings. The company is expanding beyond lawn care to become a one-stop shop for all home services, operating three brands (LawnStarter, Lawn Love, Home Gnome) on a shared platform.
You will be the engineering anchor of a product initiative, working as part of a tight cross-functional team with a Product Manager and designer, alongside engineering peers on adjacent initiatives. You own the full lifecycle: shaping the problem, deciding the technical approach, directing AI agents to implement code, shipping to production, and owning the outcome with your team. You are measured by impact and metric movement, not lines of code merged.
Key responsibilities include:
- Owning the technical approach: architecture, data model, integration choices, rollout plan, observability, and rollback strategy for your initiative
- Ensuring implementation quality through prompts, guardrails, evals, tests, and review loops that allow agents to ship safe, correct, production-ready code
- Cross-functional partnership with PM (scope and tradeoffs) and designer (UX decisions), regular collaboration with engineering peers, and weekly check-ins with your Engineering Manager
- Owning the initiative outcome—the metric the initiative was set up to move—and presenting results 2–4 weeks post-launch
- Maintaining a high bar for production correctness, security, performance, observability, and experience for both customers and service professionals
What makes this role exciting: you ship end-to-end from problem-framing through production to post-launch metric review; you work as a true product partner bringing engineering judgment to product calls; you get real autonomy with appropriate checkpoints; and you operate at a staff-level bar with trust to make calls, ship hard things, and stand behind outcomes.
Year 1 success looks like: shipping 3–4 initiatives end-to-end with at least two clearly moving their metric; building an agent workflow (prompts, evals, review loop) that peers adopt; meaningfully shorter cycle time from problem-framing to first production rollout; no customer- or pro-facing regressions from agent-authored code; and leaving behind visible artifacts (runbooks, evals, workflows, post-launch write-ups) that peers reference.
The tech stack includes AI agents (Claude Code, Cursor, Codex, internal agent stack, MCP servers, evals tooling), backend (PHP/Laravel), frontend (TypeScript/React/React Native for customer and pro apps, web and mobile), data (Redshift, dbt, Segment, Airflow), and infrastructure (AWS, Datadog, Sentry, GitHub Actions).
REQUIREMENTS:
- AI-native: Claude Code, Cursor, Codex, or equivalent are how you ship today on production work; you have real opinions about prompts, evals, agent loops, and review workflows; you've built a workflow where agents write all the code while you steer, review, and verify
- Operating at a lead/staff level: you've been the person making the call, shipping the hard thing, and standing behind whether it worked
- Outcome-driven: you measure your week in "did the metric move" and "did the experience get better"; you read post-launch dashboards and own the answer
- Strong horizontal partner: you hold your own with strong PMs and designers; you collaborate well with engineering peers in a shared codebase; you bring engineering judgment to product calls and product judgment to engineering calls
- Decisive and documented: you make architecture, data-model, and rollout calls; write them down; get fast input; and move
- Force multiplier: your impact compounds beyond your own initiative through reusable artifacts—agent workflows, evals, runbooks, post-launch reviews
- Customer- and pro-minded: you care about outcomes for both sides of the real marketplace
- Deep skill in at least one of the tech stacks (backend, frontend, data, or infra) plus credible production experience with AI coding agents
