A little more about the team:

AI agents at Honeycomb investigate, reason, and act on real observability data. They live in Canvas: the agentic workspace where engineers go to understand their systems.

The Agentic Intelligence team has shipped Canvas, the Honeycomb MCP server, and the Canvas Agent and Canvas Skills surfaces. The roadmap ahead is about refining what we’ve built and shipping more on top of it.

We're a small, senior team and we work closely across R&D to get things done. This role leans frontend — most of your work will live there, with a healthy dose of supporting backend work. We're looking for someone who takes real ownership of the user experience in Canvas, advocates for it, and knows when to pull others in.

What you'll do in the role:

• Ship AI product features. You'll own work across Canvas, our agents, and MCP, from backend infrastructure through the TypeScript frontend. The platform is real and in production. Your job is to make it better.
• Work on agent infrastructure. Agent-store, agent-service, canvas-sync: the systems everything else depends on. You'll improve them and help ship the next iterations of this work.
• Work full-stack. Golang on the backend, React/TypeScript on the frontend. You don't need to specialize in both, but you can't be blocked by either.
• Own your software in production. On-call rotation, incident investigation, closing reliability gaps before they become a problem.
• Work with design and product. We run a product triad. You'll give feedback early, make scope calls with context, and stay in the loop from kickoff through launch.

What you'll bring to the role:

• You've shipped products on top of LLMs or agents. You know what's hard about building with these systems and how to work around that.
• Strong React/TypeScript skills. You have opinions about component design and state management, and you've delivered product features end-to-end. This is your home base.
• Enough backend to not be blocked. Golang experience, or close to it. You can own a backend workstream, contribute to agent infrastructure, and hold your own in a design review.
• You care about the user experience. You bring real empathy for the user and want to own — or strongly shape — the UX in your domain. You like working closely with design and product, not just taking handoffs from them.
• You get a problem and you move. Clear scope, right questions up front, done means done. You close quality debt; you don't leave things at 80%.
• Production experience. You've shipped to prod, investigated incidents, and ideally done on-call.
• You make the team around you better. You've mentored engineers, run design reviews, raised the quality bar for a codebase. People ask you first… and a bad pun won’t ruin your day.
• You communicate clearly. Written and verbal, feedback both ways. We think this is an engineering skill, not a soft skill.

Bonus Points (Nice-to-Haves):

• Observability or developer tooling background. You've built for engineers debugging production systems and you know what the stakes are.
• Experience with evaluating agents. You've thought about how to measure whether an AI feature is actually working, not just whether it shipped.
• Familiarity with MCP or agent framework integrations. You've built on top of MCP, LangChain, or similar.

#LI-DNI