Category: Uncategorized

 

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 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

• A manual-testing role: Most of your testing work should be building automation, not executing test scripts by hand.
• A ticket-executor role: You’re expected to shape what gets worked on and how, not just process a backlog.
• A single-tool role: We use Cypress today, but we adapt. If you’re only comfortable in one framework or one part of the stack, this will be limiting.
• A role for someone who avoids ambiguity: The business is changing fast. New verticals, new flows, new edge cases. You need to be comfortable moving before everything is defined.
• A back-of-the-line role: Quality is a first-class concern here. You’re expected to have opinions and influence the process upstream, not just verify what’s already been built.

Benefits

Compensation & Benefits

• Base salary: $45-$65k
• Equity: We want you invested in the quality and long-term health of the platform you’re helping build.
• Fully remote: Work from anywhere in the Americas. Deep focus and async collaboration are the norm here.
• Unlimited PTO: We focus on results. Take what you need.

 

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.

This is a remote role for candidates located in São Paulo, Brazil

 

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 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

• A manual-testing role: Most of your testing work should be building automation, not executing test scripts by hand.
• A ticket-executor role: You’re expected to shape what gets worked on and how, not just process a backlog.
• A single-tool role: We use Cypress today, but we adapt. If you’re only comfortable in one framework or one part of the stack, this will be limiting.
• A role for someone who avoids ambiguity: The business is changing fast. New verticals, new flows, new edge cases. You need to be comfortable moving before everything is defined.
• A back-of-the-line role: Quality is a first-class concern here. You’re expected to have opinions and influence the process upstream, not just verify what’s already been built.

Benefits

Compensation & Benefits

• Base salary: $45-$65k
• Equity: We want you invested in the quality and long-term health of the platform you’re helping build.
• Fully remote: Work from anywhere in the Americas. Deep focus and async collaboration are the norm here.
• Unlimited PTO: We focus on results. Take what you need.

 

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.

This is a remote role for candidates located in Belo Horizonte, Brazil

 

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 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

• A manual-testing role: Most of your testing work should be building automation, not executing test scripts by hand.
• A ticket-executor role: You’re expected to shape what gets worked on and how, not just process a backlog.
• A single-tool role: We use Cypress today, but we adapt. If you’re only comfortable in one framework or one part of the stack, this will be limiting.
• A role for someone who avoids ambiguity: The business is changing fast. New verticals, new flows, new edge cases. You need to be comfortable moving before everything is defined.
• A back-of-the-line role: Quality is a first-class concern here. You’re expected to have opinions and influence the process upstream, not just verify what’s already been built.

Benefits

Compensation & Benefits

• Base salary: $45-$65k
• Equity: We want you invested in the quality and long-term health of the platform you’re helping build.
• Fully remote: Work from anywhere in the Americas. Deep focus and async collaboration are the norm here.
• Unlimited PTO: We focus on results. Take what you need.

 

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.

This is a remote role for candidates located in São Paulo, Brazil.

 

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 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 calls for careful, hand-written code in a sensitive area, you write it yourself.

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 — 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 know when to let the agent run versus write it yourself.
• 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 $100M+ 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.

Benefits

• Competitive salary of USD $80,000–$100,000 annual base
• Work from anywhere
• High ownership and autonomy
• Fast-moving team that loves to build, learn, and grow

This is a remote role for candidates located in Florianópolis, Brazil

 

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 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

• A manual-testing role: Most of your testing work should be building automation, not executing test scripts by hand.
• A ticket-executor role: You’re expected to shape what gets worked on and how, not just process a backlog.
• A single-tool role: We use Cypress today, but we adapt. If you’re only comfortable in one framework or one part of the stack, this will be limiting.
• A role for someone who avoids ambiguity: The business is changing fast. New verticals, new flows, new edge cases. You need to be comfortable moving before everything is defined.
• A back-of-the-line role: Quality is a first-class concern here. You’re expected to have opinions and influence the process upstream, not just verify what’s already been built.

Benefits

Compensation & Benefits

• Base salary: $45-$65k
• Equity: We want you invested in the quality and long-term health of the platform you’re helping build.
• Fully remote: Work from anywhere in the Americas. Deep focus and async collaboration are the norm here.
• Unlimited PTO: We focus on results. Take what you need.

 

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.