Senior Engineering Lead (AI-Native) - LawnStarter

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 — Median time from problem-framing to first production rollout on your initiatives is meaningfully shorter.
• Quality holds — No customer- or pro-facing regression traceable to agent-authored code that slipped through your review.
• Visible leverage — Peers point to artifacts you left behind — runbooks, evals, agent workflows, post-launch write-ups — as references they use.

Requirements

Who You Are

• AI-native. Claude Code, Cursor, Codex, or equivalent are how you ship today — daily, on production work. You have real opinions about prompts, evals, agent loops, and review workflows, and you've built a workflow where agents write all of the code while you steer, review, and verify.
• Operating at a lead level. Whatever your current title, 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 the post-launch dashboard and own the answer.
• A strong horizontal partner. 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.
• Decisive and documented. You make architecture, data-model, and rollout calls, write them down, get fast input, and move.
• A force multiplier. Your impact compounds beyond your own initiative because you leave reusable artifacts behind — agent workflows, evals, runbooks, post-launch reviews.
• Customer- and pro-minded. This is a real marketplace with real people on both sides, and you care about the outcomes for both.

Good to Know

• An individual-contributor role with room to grow. 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. You ship features that move metrics; platform and architecture work happen inside the initiative when the outcome needs them.
• Hands-on, with a high quality bar. Agents handle much of the implementation; you bring the judgment, design, safety, and accountability. The bar is high.
• Shipping to a live marketplace. With $150M+ in bookings, customers and pros use what you ship within the same week.

Tech You'll Touch

• 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 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.