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
Company values
Happy to Help
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
Craft over Convention
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
Progress not Perfection
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
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