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About LawnStarter
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M 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've spent the last two years rebuilding the platform from the ground up: new customer app, new Pro onboarding, job distribution engine, and data infrastructure. The foundation is solid. Now we're moving into expanded service verticals, dynamic pricing, and AI-powered features at scale. Quality needs to keep up.
Our stack is PHP (Laravel) and React/React Native, with Cypress as our primary test framework. We actively use Claude Code as part of our engineering workflow, not as an experiment, but as a core part of how we work.
The Role
As we scale into new service verticals and grow the engineering team, quality can't be reactive. This role exists to make quality proactive: owning the automation coverage, shaping the bug process, and partnering closely with Product, Design, and the Data team to catch problems before they reach customers.
You'll report to the Engineering Manager and be embedded across the engineering organization. This is hands-on and strategic, helping raise the quality ceiling for the whole team.
What makes this role different:
What You'll Own
Problems to Solve
Keeping automation coverage sharp as the product evolves Our E2E coverage is solid. The challenge is staying ahead: bringing new ideas, exploring different types of coverage, and finding smarter ways to automate different aspects of the application as the product grows and new service verticals come online.
Exploring new tools and leveraging AI to improve quality operations AI is changing how we build software, and it's changing how we can test it too. There's real opportunity here to use AI tools to increase output quality, cut repetitive work, and discover coverage gaps we'd otherwise miss. We want someone who actively explores that space, not someone who waits to be told what to use.
What Success Looks Like (Year 1)
Who You Are
AI-native. You use AI tools like Claude Code to write, maintain, and extend test code, not as a novelty, but as a standard part of how you work. You're comfortable with browser MCPs, agent workflows, and building scripts that automate the boring parts of QA. This is unlikely to be a good fit if you're skeptical of AI in the engineering workflow.
Automation-first. Your default when you find a problem is to ask how to prevent it automatically. You build test infrastructure that outlasts any individual test case. This is unlikely to be a good fit if your strength is manual testing and automation is something you tolerate rather than drive.
Customer-obsessed. You think like a user. When you're testing a flow, you're asking whether it actually works for a real customer, not just whether it matches the spec. This is unlikely to be a good fit if you treat quality as a checkbox rather than a standard.
Adaptable. You don't get attached to a single tool, stack, or process. We're in a period of rapid evolution in how software gets built and tested, especially with AI, and you lean into that rather than resist it. This is unlikely to be a good fit if you thrive only in stable, well-defined tooling environments.
Collaborative and direct. You work across Product, Design, Engineering, and Operations. You surface risks early, speak plainly about what you find, and push back when something isn't ready to ship. This is unlikely to be a good fit if you prefer to work in isolation or soften feedback to avoid friction.
Technically solid. You're comfortable reading and writing code. You understand front and backend web services well enough to test them properly, write meaningful SQL queries, and debug issues across the stack. This is unlikely to be a good fit if your technical depth is limited to a single layer or framework.
This Role Is NOT
LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment.