Staff Software Engineer, Product

LawnStarter; Rio de Janeiro; On-site; BRL 120,000 - 180,000; Full time

Job summary

LawnStarter is seeking an engineering anchor for a staff-level initiative, working with a PM and designer to shape problems, decide technical approaches, and guide AI agents to implement code, ship to production, and own the eventual outcome with the team.

You will be measured by impact—ensuring strong tests, controlled rollouts, and safety through review gates—while iterating toward measurable metrics that move the business.

Responsibilities

• You’ll be the engineering anchor of an initiative, collaborating with a PM and designer to shape problems and decide technical approaches.
• You’ll direct AI agents to implement code, ship to production, and own the outcome with your team.
• You’ll ensure strong tests, evals, and safe deployments with guardrails and review gates.
• You’ll be measured by impact, not lines of code merged, and defend changes with data and rollback plans.

Job description

About LawnStarter

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 LawnStarter

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. The whole team owns whether the work moves its metric. 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.

The Role

You're the engineering anchor of an initiative — 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 — with your team — owning the outcome.

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 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.

What Makes This Role Exciting

• You ship end-to-end. From problem-framing through production to the post-launch metric review — you see the whole arc and own the result with your team
• You work as a true product partner. You sit at the table with PM and design, bringing engineering judgment to product calls and product sense to engineering calls
• You get real autonomy — with the right checkpoints. You make most technical calls yourself, with architect review on significant architectural decisions and fast input from peers
• You operate at a staff bar. You're trusted to make the call, ship the hard thing, and stand behind the outcome

What You'll Own

• The technical approach — 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
• Implementation quality — 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 — held to the same standard as the rest of the team working in a shared codebase
• 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
• The initiative outcome — the metric the initiative was set up to move. 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. Agents accelerate you; they don't lower the bar

Problems to Solve

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. How do you build a workflow that lets a small team ship far more than its size would suggest?

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. How do you move fast, keep your team aligned, and stay accountable to the outcome?

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. 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 2-4 weeks after launch?

What Success Looks Like (Year 1)

• 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)
• Agent workflow that travels — The prompts, evals, and review loop you built are picked up by peers on other initiatives
• Faster cycle time —