[Hiring] Senior Software Engineer II - Agentic Intelligence @Honeycomb.io

Role Description

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. What we're looking for today is someone who brings deep agent expertise and uses it to expand what the team can build:

• New agents
• New surface area in Canvas
• Memory
• Spatial awareness
• Improved performance on our Bedrock loop

Honeycomb's data store is fast and accepts high cardinality data; that's what makes agents built on top of it different from anything built on a conventional observability backend. This role is about taking advantage of that by building agents that can do things no other observability product can do because the underlying data makes it possible.

Some of this work will start as a prototype. The expectation is that the code makes it through the full arc, from the rough first version through to something that holds up in production.

What you'll do

• Design and deliver production-grade agents.
• Build agents that investigate, reason, and act on live observability data inside Canvas.
• Own the agent work; support the whole product.
• Scope, build, ship, and maintain the agents including the evals that tell you whether they got better or are just different.
• Build agents only Honeycomb can build.
• Extend the surface, and decide what's next.
• Define what "good" means for agents here.

Example projects

• Multiple agents collaborating on one shared Canvas investigation.
• Auto-investigation the moment an SLO burn alert fires.
• Skills that encode a team's domain expertise.

Qualifications

• AI and agent engineering experience.
• End-to-end ownership.
• Current judgment, not just past experience.
• Agent architecture depth.
• Product judgment.

Requirements

• You've shipped LLM-based systems people relied on in production.
• On a small team there's no handoff queue.
• You have informed opinions about what's shifted in agent design in the last six to twelve months.
• You understand how a fast, high-cardinality data store changes what an agent can reason about.
• You can look at what the agent layer does today and see what it should do next.

Benefits

• A stake in our success - generous equity with employee-friendly stock program.
• Transparent pay based on levels relative to experience.
• Unlimited PTO.
• Home office, co-working, and internet stipend.
• Full benefits coverage for employees, with additional coverage available for dependents.
• Up to 16 weeks of paid parental leave, regardless of path to parenthood.
• Annual development allowance.
• And much more...