Business Services & Consulting • all cities, CO 6
This role sits on our Agents team — the group building the AI-powered experiences our customers use every day. It's where large language models stop being a demo and become a dependable part of the product: answers, drafts, and assistive features that have to be fast, trustworthy, and genuinely useful in front of real customers.
We're looking for an engineer who can build that end-to-end — from the retrieval and agentic plumbing behind a feature to the polished, fast, accessible interface a support agent actually touches. You'll have a frontend center of gravity (strong JavaScript/TypeScript and real front-end craft) while operating as a full-stack product engineer who owns the whole thing.
Engineers at Help Scout own their work end-to-end — from problem definition through production, and from the customer signal that shaped the bet to the dashboard that tells you whether it landed. You're full-stack capable, customer-fluent, and genuinely fast with AI tools. You ship the whole thing, not just your layer.
We develop in Kotlin, Java, TypeScript, React, and PHP.
We leverage Elasticsearch, ClickHouse, Kafka, Flink, RabbitMQ, MySQL, and PostgreSQL — plus vector search and LLM tooling for our AI features.
We're heavy users of the AWS ecosystem, Slack, GitHub, and Linear.
We use AI tools — Cursor, Claude Code, and a growing set of internal tools — every day. We treat our AI workflow as a product we're shaping together, and we expect you to help shape it.
Teams are small and project-based. You might be 3 engineers and a PM on one project, 2 engineers and a designer on the next, or holding more of the product thinking yourself. You're expected to operate well across different compositions and ship end-to-end.
Ship customer-facing AI features across the full stack. Build the experience and what's behind it — LLM-backed flows, retrieval (RAG) and embeddings, and agentic tool-calling — through to a fast, accessible, well-crafted UI. Most engineers here have a strength; yours leans frontend, and you take initiative across every layer.
Treat AI output as something you verify, not assume. Build evaluations, guardrails, and monitoring into AI features so quality is measurable and holds up in production. Know when to trust a model's output and when to push back — and design the system so the product degrades gracefully when a model or provider misbehaves.
Sweat the front-end craft. The interface is where AI either earns or loses customer trust. You care about responsiveness, streaming/loading states, accessibility, and the small details that make an assistive feature feel reliable rather than gimmicky.
Stay close to customers. Join customer calls when more context is needed to design the right solution, participate in a support escalation rotation, and watch session recordings on the features you own. Bring that signal back into the work.
Own outcomes, not just output. Before a project starts, you and a PM agree on a specific outcome metric the work is trying to move. You instrument it, and after it ships you're watching the dashboard and talking to customers — deciding what to do next, not just closing a Linear issue.
Co-author the solution. PMs own strategy and sequencing; you bring the judgment and craft for how solutions actually get built, and you'll often shape the product thinking too. The best work happens when engineers, designers, and PMs support each other.
Own production readiness from the start. Automation, reliability, monitoring, alerting, and logging aren't afterthoughts — they're part of how you ship. The work continues after merge.
Use AI tools every day as part of your craft. We expect fluency with Cursor, Claude Code, or similar. If you saw a way to make the team's AI workflow better tomorrow, you'd say so.
Help us hire. From time to time, partner with our Talent team to interview future teammates — one of the highest-leverage things any engineer here does.
You're a strong, full-stack-capable engineer with a frontend center of gravity. You have substantial JavaScript/TypeScript and React experience and real front-end craft, and you take initiative across the stack and ship end-to-end without waiting for someone to own the other half.
You've built real things with LLMs — not just prototypes. You're comfortable with retrieval (RAG), embeddings, prompt/context engineering, and agentic/tool-calling patterns, and you have a point of view on what makes AI features actually reliable in production.
You hold a high bar for AI quality. You think in terms of evaluation, verification, and guardrails; you measure whether an AI feature works rather than assuming it does; and you know that more AI-generated code is not the same as more value.
You're genuinely fluent with AI coding tools and treat them as part of your craft. You move fast without sacrificing judgment, and you have a point of view on where these tools are heading.
You're customer-fluent. You actively seek out customer signal because it's how you stay connected to whether your work is actually solving the problem.
You own outcomes. You measure your work by whether it landed — and you're comfortable saying "this didn't move what we hoped, here's what we want to try next," then following through.
You take real ownership of the full development lifecycle — automation, reliability, resilience, monitoring, alerting, and logging built in from the start. You stay with what you ship until the metric moves and the customer is better off.
You communicate clearly in writing and in conversation. Help Scout is fully remote and writing is the medium of most decisions. You give and receive direct feedback, and you see code review and pairing as real chances to teach and learn.
Experience making LLM features production-grade: latency/cost tuning, fallbacks and circuit breakers across providers, moderation/safety, and handling sensitive data responsibly.
Familiarity with evaluation/observability tooling for AI (LLM-as-judge, test sets, tracing) and the discipline of building representative eval sets.
Design sensibility — comfort partnering closely with designers and elevating the craft of an interface, not just implementing a spec.
Experience in customer support, productivity, or other tools where trust and reliability are the product.
Help is in our first name! We show up for each other — not out of obligation, but because we're invested in the team's collective success. We share knowledge freely, lead with generosity, and practice empathy with our teammates, customers, and community.
Our success relies on the quality and craft of the work we put into the world. The status quo simply won't work. So we insist on narrow focus, sweating every detail, and relentless pursuit of customer delight.
Achieving our true potential — collectively and individually — requires constant progress and forward momentum. By creating a culture of curiosity and openness, we aim to create a safe space for mistakes, the ability to identify them quickly, and use them to get better.
Own the outcome means taking full responsibility for the results of your work, decisions, and contributions. It reflects a mindset of accountability, proactiveness, and follow-through. If you "own the outcome," you don't just complete tasks, you ensure your work leads to meaningful results, and take initiative to solve problems rather than passing them along.
Read more about how we define, share, and live these values here.
This role sits on our Agents team — the group building the AI-powered experiences our customers use every day. It's where large language models stop being a demo and become a dependable part of the product: answers, drafts, and assistive features that have to be fast, trustworthy, and genuinely useful in front of real customers.
We're looking for an engineer who can build that end-to-end — from the retrieval and agentic plumbing behind a feature to the polished, fast, accessible interface a support agent actually touches. You'll have a frontend center of gravity (strong JavaScript/TypeScript and real front-end craft) while operating as a full-stack product engineer who owns the whole thing.
Engineers at Help Scout own their work end-to-end — from problem definition through production, and from the customer signal that shaped the bet to the dashboard that tells you whether it landed. You're full-stack capable, customer-fluent, and genuinely fast with AI tools. You ship the whole thing, not just your layer.
We develop in Kotlin, Java, TypeScript, React, and PHP.
We leverage Elasticsearch, ClickHouse, Kafka, Flink, RabbitMQ, MySQL, and PostgreSQL — plus vector search and LLM tooling for our AI features.
We're heavy users of the AWS ecosystem, Slack, GitHub, and Linear.
We use AI tools — Cursor, Claude Code, and a growing set of internal tools — every day. We treat our AI workflow as a product we're shaping together, and we expect you to help shape it.
Teams are small and project-based. You might be 3 engineers and a PM on one project, 2 engineers and a designer on the next, or holding more of the product thinking yourself. You're expected to operate well across different compositions and ship end-to-end.
Ship customer-facing AI features across the full stack. Build the experience and what's behind it — LLM-backed flows, retrieval (RAG) and embeddings, and agentic tool-calling — through to a fast, accessible, well-crafted UI. Most engineers here have a strength; yours leans frontend, and you take initiative across every layer.
Treat AI output as something you verify, not assume. Build evaluations, guardrails, and monitoring into AI features so quality is measurable and holds up in production. Know when to trust a model's output and when to push back — and design the system so the product degrades gracefully when a model or provider misbehaves.
Sweat the front-end craft. The interface is where AI either earns or loses customer trust. You care about responsiveness, streaming/loading states, accessibility, and the small details that make an assistive feature feel reliable rather than gimmicky.
Stay close to customers. Join customer calls when more context is needed to design the right solution, participate in a support escalation rotation, and watch session recordings on the features you own. Bring that signal back into the work.
Own outcomes, not just output. Before a project starts, you and a PM agree on a specific outcome metric the work is trying to move. You instrument it, and after it ships you're watching the dashboard and talking to customers — deciding what to do next, not just closing a Linear issue.
Co-author the solution. PMs own strategy and sequencing; you bring the judgment and craft for how solutions actually get built, and you'll often shape the product thinking too. The best work happens when engineers, designers, and PMs support each other.
Own production readiness from the start. Automation, reliability, monitoring, alerting, and logging aren't afterthoughts — they're part of how you ship. The work continues after merge.
Use AI tools every day as part of your craft. We expect fluency with Cursor, Claude Code, or similar. If you saw a way to make the team's AI workflow better tomorrow, you'd say so.
Help us hire. From time to time, partner with our Talent team to interview future teammates — one of the highest-leverage things any engineer here does.
You're a strong, full-stack-capable engineer with a frontend center of gravity. You have substantial JavaScript/TypeScript and React experience and real front-end craft, and you take initiative across the stack and ship end-to-end without waiting for someone to own the other half.
You've built real things with LLMs — not just prototypes. You're comfortable with retrieval (RAG), embeddings, prompt/context engineering, and agentic/tool-calling patterns, and you have a point of view on what makes AI features actually reliable in production.
You hold a high bar for AI quality. You think in terms of evaluation, verification, and guardrails; you measure whether an AI feature works rather than assuming it does; and you know that more AI-generated code is not the same as more value.
You're genuinely fluent with AI coding tools and treat them as part of your craft. You move fast without sacrificing judgment, and you have a point of view on where these tools are heading.
You're customer-fluent. You actively seek out customer signal because it's how you stay connected to whether your work is actually solving the problem.
You own outcomes. You measure your work by whether it landed — and you're comfortable saying "this didn't move what we hoped, here's what we want to try next," then following through.
You take real ownership of the full development lifecycle — automation, reliability, resilience, monitoring, alerting, and logging built in from the start. You stay with what you ship until the metric moves and the customer is better off.
You communicate clearly in writing and in conversation. Help Scout is fully remote and writing is the medium of most decisions. You give and receive direct feedback, and you see code review and pairing as real chances to teach and learn.
Experience making LLM features production-grade: latency/cost tuning, fallbacks and circuit breakers across providers, moderation/safety, and handling sensitive data responsibly.
Familiarity with evaluation/observability tooling for AI (LLM-as-judge, test sets, tracing) and the discipline of building representative eval sets.
Design sensibility — comfort partnering closely with designers and elevating the craft of an interface, not just implementing a spec.
Experience in customer support, productivity, or other tools where trust and reliability are the product.
Help is in our first name! We show up for each other — not out of obligation, but because we're invested in the team's collective success. We share knowledge freely, lead with generosity, and practice empathy with our teammates, customers, and community.
Our success relies on the quality and craft of the work we put into the world. The status quo simply won't work. So we insist on narrow focus, sweating every detail, and relentless pursuit of customer delight.
Achieving our true potential — collectively and individually — requires constant progress and forward momentum. By creating a culture of curiosity and openness, we aim to create a safe space for mistakes, the ability to identify them quickly, and use them to get better.
Own the outcome means taking full responsibility for the results of your work, decisions, and contributions. It reflects a mindset of accountability, proactiveness, and follow-through. If you "own the outcome," you don't just complete tasks, you ensure your work leads to meaningful results, and take initiative to solve problems rather than passing them along.
Read more about how we define, share, and live these values here.
