Staff Software Engineer, Product (São Paulo)

Staff Software Engineer, Product (Remote - São Paulo)

Company: LawnStarter Location: Brazil (Remote - São Paulo candidates) Type: Permanent Seniority: Senior

About LawnStarter

LawnStarter is the leading on-demand marketplace for lawn care and outdoor services, facilitating over $100 million in annual bookings. We are expanding to become the go-to platform for all home services, operating across three brands (LawnStarter, Lawn Love, Home Gnome) on a unified platform.

About Engineering at LawnStarter

We operate in small, focused initiative teams, where Product Engineers collaborate closely with Product Managers and Designers. Supported by Engineering Managers who foster growth, you'll also work alongside engineering peers across initiatives within a shared codebase. The entire team shares ownership of the success metrics for their work.

AI coding agents are a significant advantage, empowering our small, senior team to deliver more, faster, and with a higher standard of quality. We seek engineers who are driven by ownership and energized by shipping impactful solutions to a real marketplace serving both customers and service professionals.

The Role

As the engineering anchor for an initiative, you will be an integral part of a focused team, working alongside your Product Manager and Designer, and collaborating with engineering peers on adjacent initiatives. You will be involved in the entire product lifecycle: shaping the problem, defining the technical approach, directing AI agents for code implementation, deploying to production, and ultimately, owning the outcome with your team.

Your performance will be measured by impact, not just lines of code. When an AI agent can safely ship a feature, your role is to ensure it's done correctly and drives the desired metric. In areas requiring meticulous, hand-written code, you will be responsible for that implementation.

What Makes This Role Exciting:

• End-to-End Ownership: From problem definition through production and post-launch metric review, you will see the entire arc and own the results with your team.
• True Product Partnership: You will actively participate in product decisions, bringing engineering expertise to product discussions and product sensibility to engineering challenges.
• Autonomy with Support: You will make most technical decisions independently, with architectural reviews for significant decisions and prompt input from peers.
• Staff-Level Impact: You are trusted to make critical decisions, ship complex features, and stand behind the results.

What You'll Own:

• Technical Approach: Define the architecture, data model, integration strategies, rollout plans, observability, and rollback strategies for your initiative. You will make most decisions, seeking architect review for significant architectural choices, documenting them, and iterating based on data.
• Implementation Quality: Ensure the quality of AI-generated code through effective prompts, guardrails, evaluations, tests, and review processes. You are accountable for the code, held to the same high standards as hand-written code.
• Cross-Functional Partnership: Engage daily with your Product Manager on scope and tradeoffs, with your Designer on UX and prototyping, collaborate regularly with engineering peers, and have weekly check-ins with your Engineering Manager.
• Initiative Outcomes: Be accountable for the key metric your initiative is designed to move. You will present results and findings with your Product Manager 2-4 weeks post-launch.
• High Shipping Bar: Maintain production correctness, security, performance, observability, and a positive experience for customers and professionals. AI agents are accelerators, not compromises on quality.

Problems You'll Solve:

• Leading AI Agents: Master the craft of using AI agents to produce code at a staff-level quality bar, focusing on prompts, evaluations, and review workflows that ensure safe, production-ready code.
• Autonomous Decision-Making: Navigate making and documenting technical decisions rapidly, balancing autonomy with peer review and accountability for outcomes.
• Shipping Outcomes: Focus on driving key metrics, making disciplined scope decisions, and following up rigorously on post-launch performance.

What Success Looks Like (Year 1):

• Successfully ship 3-4 initiatives end-to-end, with at least two demonstrably moving their target metrics.
• Develop and share AI agent workflows that are adopted by peers on other initiatives.
• Significantly reduce the median cycle time from problem definition to production rollout.
• Maintain high quality standards, with no customer- or professional-facing regressions attributable to AI-authored code.
• Create visible, reusable artifacts (runbooks, evaluations, agent workflows, post-launch reviews) that serve as valuable references for peers.

Requirements:

• AI Proficiency: Daily, production-level experience with AI coding agents (e.g., Claude Code, Cursor, Codex). You have strong opinions on prompts, evaluations, and agent workflows, and know when to leverage AI versus write code manually.