Job Description
Senior Quality Engineer 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: Cross-functional influence : You work directly with engineers, PMs, Designers, and the Data team to surface risks early, understand requirements deeply, and propose better solutions. You may be embedded in one or two delivery teams. Automation ownership : You own the strategy for building and expanding our automated test coverage, not just executing test cases someone else wrote. Process shaper : You'll identify gaps in our quality process and have the authority to fix them, not just flag them. What You'll Own Automated test coverage : Design and grow our E2E and integration test suite using Cypress. Set the strategy, prioritize coverage gaps, and raise the floor across the platform. Bug process : Own the full lifecycle from triage to production fix. Keep it moving, keep stakeholders informed, and make sure nothing falls through the cracks. Cross-functional quality partnership : Collaborate with PMs, Designers, and the Data team to review requirements, raise risks early, and define what "done" actually means. Process improvement : Identify weaknesses in how we deliver software and propose solutions. Not just document them. Exploratory testing : Test new features and existing flows on web and mobile across devices, catching what automation misses. 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) Automation coverage stays strong : New features ship with automated tests as a baseline, and coverage strategies evolve with the product. Bug process runs smoothly : Triage to fix cycle is predictable and stakeholders know where things stand without having to ask. Deep business knowledge : You understand how the marketplace works well enough to anticipate edge cases on your own. Quality process improvement shipped : At least one meaningful improvement to how we deliver quality is in place and being used by the team. Requirements 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 peri
