Senior Software Engineer (Agentic Intelligence)

Join Honeycomb, a leading observability platform, as a Senior Software Engineer focused on Agentic Intelligence. In this role, you will design and deliver production-grade agents that investigate, reason, and act on live observability data. You will own the agent work, support the entire product, and build agents that only Honeycomb can create. This position offers a range of benefits, including unlimited PTO, 100% covered medical, dental, and vision insurance, and a generous equity package.

Product judgment. You can look at what the agent layer does today and see what it should do next and make that case with a prototype, not a deck

About Honeycomb

Build agents that only Honeycomb can build, using a data store that returns high-cardinality queries in seconds to reason over signals that a conventional backend can't serve at this fidelity.

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

Agent architecture depth. You understand how a fast, high-cardinality data store changes what an agent can reason about, and how to design for that

End-to-end ownership. On a small team there's no handoff queue. You can take something from rough prototype to production-grade without needing someone behind you to do the durable engineering

Current judgment, not just past experience. You have informed opinions about what's shifted in agent design in the last six to twelve months that would change how you'd build today

Responsibilities

• Design and deliver production-grade 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 evaluations that tell you whether they got better or are just different.

Requirements

• AI and agent engineering experience. You've shipped LLM-based systems people relied on in production not demos, not fine-tuned models in a research context. You know where agent systems break and how to design around it
• Observability or developer-tools background. Engineers are your users; you'll ramp faster with fluency in that world, and the work is better
• Familiarity with eval frameworks, agent tooling, RAG, and prompt engineering