About Engineering at LawnStarter<\/strong><\/p>\nWe 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. The whole team owns whether the work moves its metric.<\/p>\n
AI coding agents are a force multiplier here \u2014 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.<\/p>\n
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The Role<\/strong><\/p>\nYou’re the engineering anchor of an initiative \u2014 working as part of a tight team with your PM and designer, and alongside engineering peers on adjacent initiatives. You have a hand in the full lifecycle: shaping the problem, deciding the technical approach, directing AI agents to implement much of the code, shipping to production, and \u2014 with your team \u2014 owning the outcome.<\/p>\n
You’re measured by impact, not by lines of code merged. When an agent can ship something safely, your job is to make sure it’s done right and the metric moves. When the work calls for careful, hand-written code in a sensitive area, you write it yourself.<\/p>\n
What makes this role exciting:<\/strong><\/p>\n\n- You ship end-to-end.<\/strong> From problem-framing through production to the post-launch metric review \u2014 you see the whole arc and own the result with your team.<\/li>\n
- You work as a true product partner.<\/strong> You sit at the table with PM and design, bringing engineering judgment to product calls and product sense to engineering calls.<\/li>\n
- You get real autonomy \u2014 with the right checkpoints.<\/strong> You make most technical calls yourself, with architect review on significant architectural decisions and fast input from peers.<\/li>\n
- You operate at a staff bar.<\/strong> You’re trusted to make the call, ship the hard thing, and stand behind the outcome.<\/li>\n<\/ul>\n
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What You’ll Own<\/p>\n
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\n- The technical approach<\/strong> \u2014 architecture, data model, integration choices, rollout plan, observability, and rollback strategy for your initiative. You make most calls yourself and bring significant architectural decisions to architect review; you document them, and revisit if the data says you were wrong.<\/li>\n
- Implementation quality<\/strong> \u2014 the prompts, guardrails, evals, tests, and review loop that let agents ship safe, correct, production-ready code. Most lines will be agent-authored, and you’re accountable for them \u2014 held to the same standard as the rest of the team working in a shared codebase.<\/li>\n
- Cross-functional partnership<\/strong> \u2014 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.<\/li>\n
- The initiative outcome<\/strong> \u2014 the metric the initiative was set up to move. With your PM, you present results 2\u20134 weeks post-launch and share the “did it work” answer.<\/li>\n
- A high bar for what ships<\/strong> \u2014 production correctness, security, performance, observability, and the experience for customers and pros. Agents accelerate you; they don’t lower the bar.<\/li>\n<\/ul>\n
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Problems to Solve<\/p>\n
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Leading AI agents at a staff-level quality bar<\/strong> 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. How do you build a workflow that lets a small team ship far more than its size would suggest?<\/p>\nOwning decisions with high autonomy<\/strong> You have real latitude to make and document technical calls quickly \u2014 with architect review on the big architectural ones and peers to pressure-test your thinking. How do you move fast, keep your team aligned, and stay accountable to the outcome?<\/p>\nShipping outcomes, not features<\/strong> Each initiative is measured by a metric \u2014 a conversion rate, a retention curve, a pro-funnel KPI, a unit-economics shift. You’re accountable for the number alongside your team. How do you scope to actually move it, decide what *not* to build, and have the discipline to follow up 2\u20134 weeks after launch?<\/p>\n\u00a0<\/p>\n
What Success Looks Like (Year 1)<\/p>\n
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\n- Initiative outcomes hit<\/strong> \u2014 You’ve shipped 3\u20134 initiatives end-to-end, and at least two clearly moved their metric (with the post-launch review to prove it).<\/li>\n
- Agent workflow that travels<\/strong> \u2014 The prompts, evals, and review loop you built are picked up by peers on other initiatives.<\/li>\n
- Faster cycle time<\/strong> \u2014 Median time from problem-framing to first production rollout on your initiatives is meaningfully shorter.<\/li>\n
- Quality holds<\/strong> \u2014 No customer- or pro-facing regression traceable to agent-authored code that slipped through your review.<\/li>\n
- Visible leverage<\/strong> \u2014 Peers point to artifacts you left behind \u2014 runbooks, evals, agent workflows, post-launch write-ups \u2014 as references they use.<\/li>\n<\/ul>\n
Requirements<\/strong><\/p>\nWho You Are<\/p>\n
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\n- AI-native.<\/strong> Claude Code, Cursor, Codex, or equivalent are how you ship today \u2014 daily, on production work. You have real opinions about prompts, evals, agent loops, and review workflows, and you know when to let the agent run versus write it yourself.<\/li>\n
- Operating at a lead level.<\/strong> Whatever your current title, you’ve been the person making the call, shipping the hard thing, and standing behind whether it worked.<\/li>\n
- Outcome-driven.<\/strong> You measure your week in “did the metric move” and “did the experience get better.” You read the post-launch dashboard and own the answer.<\/li>\n
- A strong horizontal partner.<\/strong> You hold your own with a strong PM and designer, and you collaborate well with engineering peers in a shared codebase. You bring engineering judgment to product calls and product judgment to engineering calls.<\/li>\n
- Decisive and documented.<\/strong> You make architecture, data-model, and rollout calls, write them down, get fast input, and move.<\/li>\n
- A force multiplier.<\/strong> Your impact compounds beyond your own initiative because you leave reusable artifacts behind \u2014 agent workflows, evals, runbooks, post-launch reviews.<\/li>\n
- Customer- and pro-minded.<\/strong> This is a real marketplace with real people on both sides, and you care about the outcomes for both.<\/li>\n<\/ul>\n
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Good to Know<\/p>\n
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\n- An individual-contributor role with room to grow.<\/strong> People management sits with the EM \u2014 but the path into management is an open door for those who want it.<\/li>\n
- A product-engineering role, end-to-end.<\/strong> You ship features that move metrics; platform and architecture work happen inside the initiative when the outcome needs them.<\/li>\n
- Hands-on, with a high quality bar.<\/strong> Agents handle much of the implementation; you bring the judgment, design, safety, and accountability. The bar is high.<\/li>\n
- Shipping to a live marketplace.<\/strong> With $100M+ in bookings, customers and pros use what you ship within the same week.<\/li>\n<\/ul>\n
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Tech You’ll Touch<\/p>\n
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\n- AI agents<\/strong> \u2014 Claude Code, Cursor, Codex, internal agent stack, MCP servers, evals tooling<\/li>\n
- Backend<\/strong> \u2014 PHP\/Laravel<\/li>\n
- Frontend<\/strong> \u2014 TypeScript\/React\/React Native (customer & pro apps, web and mobile)<\/li>\n
- Data<\/strong> \u2014 Redshift, dbt, Segment, Airflow<\/li>\n
- Infra<\/strong> \u2014 AWS, Datadog, Sentry, GitHub Actions<\/li>\n
- Documentation & process<\/strong> \u2014 Brain (Claude Code skills + docs repo), Confluence, Jira<\/li>\n<\/ul>\n
You don’t need every box checked. You need deep skill in at least one of our stacks plus credible production experience with AI coding agents.<\/p>\n
Benefits<\/strong><\/p>\n\n- Competitive salary of USD $80,000\u2013$100,000 annual base<\/li>\n
- Work from anywhere<\/li>\n
- High ownership and autonomy<\/li>\n
- Fast-moving team that loves to build, learn, and grow<\/li>\n<\/ul>\n<\/div>\n