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Headquarters: Porto Alegre, State of Rio Grande do Sul, Brazil URL: http://lawnstarter.com 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 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 To apply: https://weworkremotely.com/remote-jobs/lawnstarter-staff-software-engineer-product-4

Headquarters: Campinas, State of São Paulo, Brazil URL: http://lawnstarter.com 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 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 To apply: https://weworkremotely.com/remote-jobs/lawnstarter-staff-software-engineer-product-3

Headquarters: Belo Horizonte, State of Minas Gerais, Brazil URL: http://lawnstarter.com 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 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 To apply: https://weworkremotely.com/remote-jobs/lawnstarter-staff-software-engineer-product-2

Headquarters: Florianópolis, State of Santa Catarina, Brazil URL: http://lawnstarter.com 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 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 To apply: https://weworkremotely.com/remote-jobs/lawnstarter-staff-software-engineer-product-1

Headquarters: São Paulo, Brazil URL: http://lawnstarter.com 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 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 To apply: https://weworkremotely.com/remote-jobs/lawnstarter-staff-software-engineer-product

Headquarters: new york, New York, United States URL: http://bask.health Bask is the best telehealth platform on the market. Our mission is to empower entrepreneurs and businesses to launch a DTC telemedicine company quickly and easily, abstracting away the complexities of the industry into an easy-to-use platform. With hundreds of features to meet the unique needs of telehealth, we put all the tools in our customers' hands for success. We are a rapidly growing startup, and we work with care and intention to create a high-performance company with the following in mind: Speed Wins. Make decisions, move quickly, and know that if things go wrong, it's okay for you and the company. Intuition, Then Data. We're a data-driven company. We start with our instincts and then use data to validate our decisions and improve Miles, Not Inches. Thinking small is a self-fulfilling prophecy. Favor bold ideas over incremental changes Customer-Obsession. We are obsessed with helping all our customers launch multi-billion-dollar companies with ease. We accomplish this by knowing our customers incredibly well and finding ways to make their businesses better. Ownership & Accountability, together. We embody a culture of extreme ownership, accountability, and teamwork. We count on every team member to take responsibility for their work, embrace a proactive mindset to overcome challenges, and work together to achieve our collective success A look into the day to day: Building logic based questionnaires (asynchronous visits) through the Bask software (if you have experience with building logic based workflows, you'll be great at this!) Helping customers launch and upsell medications from start to to finish Triaging bask software and order related questions and issues that come up for any of your accounts, any time of the day We're Looking For A self-starter who is passionate about enhancing the customer experience; you take pride in demonstrating the value of the products and services to your customers. You think outside of the box, excel at creative problem-solving, and are comfortable taking on projects that you have potentially never done before! You are a people-person - empathetic, self-aware, low ego, and extremely positive - and are comfortable working in a small collaborative team where you'll wear many hats. You are passionate and proficient in written and spoken communications. Customer Success at Bask Health is AI-first and customer-obsessed. Work starts in an LLM to clarify intent and context, moves into research and execution, is validated with real patients and partners, and is continuously refined as we learn. AI and self-serve research are default parts of the workflow, not side experiments. We are looking for Customer Success people who take full ownership of their accounts, treat AI as a real collaborator, and care deeply about the patient and provider experience. What You'll Do Work AI-first: Use LLMs as your starting point to draft communications, structure problems, and think through customer needs. Apply your own judgment to refine the output and make it human. Validate with real customers and iterate: Test your approaches with real patients, providers, and partners. Use what you learn to improve how we serve them — before issues become patterns. Make automation legible and trustworthy: Help customers understand what Bask's platform is doing on their behalf. Communicate clearly and proactively to build trust while keeping their experience simple. Share AI-native workflows: Document prompts, processes, and workflows that work. Share them across the Customer Success team so we raise the bar together. In this job, you will: Build strong relationships with Bask's customers and own the full customer lifecycle, including onboarding, value realization, engagement strategies, expansion, and renewals Partner with customer stakeholders to develop custom engagement initiatives that drive user adoption and support the unique needs of their patients Manage all current customer data Analyze customer engagement metrics and use them to communicate value, trends, and opportunities with key stakeholders Deliver program demos, provide insightful technical answers, and recommend creative ways to get the most out of the Bask platform Finding comfort in working in a fast-paced startup environment Believing no task is too small and no task is too tall Requirements 7+ years of experience in Customer Success, Client Services, or Customer Success at a SaaS organization Experience in e-commerce Experience with large- to enterprise-sized customer book of business Creative problem solver with a determination to succeed Independent and motivated, with the wisdom to seek help where needed An entrepreneur Highly articulate, ability to communicate effectively both when speaking and writing Highly organized, with the ability to juggle multiple projects in a fast-paced environment Comfortable collaborating with different teams (product, sales, marketing, etc.) To apply: https://weworkremotely.com/remote-jobs/bask-health-senior-customer-success-manager