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Honeycomb.io

Senior Software Engineer II – Agentic Intelligence

Honeycomb.io

Senior Software Engineer II building high-performance observability tools at Honeycomb. Design and deliver production-grade AI agents for real-time data analysis and system understanding.

Posted 7/31/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $183,340 - $206,000 per yearWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI and agent engineering, with a focus on building production-grade systems that leverage high-cardinality data stores. Capable of end-to-end ownership of agent development, from prototyping to deployment, while maintaining a strong understanding of observability and developer tools.

Highest-signal resume keywords
AI And Agent Engineering ExperienceEnd-To-End OwnershipAgent Architecture DepthProduct JudgmentObservability Background

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
LLM-Based SystemsAgent DesignHigh-Cardinality Data StorePrototype DevelopmentEval Frameworks
Soft Skills
Current Judgment
Tools & Technologies
Agent ToolingPrompt Engineering
Industry Keywords
ObservabilityDeveloper Tools

About the role

Key responsibilities & impact
  • Design and deliver production-grade agents. Build agents that investigate, reason, and act on live observability data inside Canvas. These agents must be trustworthy to engineers in high pressure situations, including mid-incident. Take one from rough first version to something that holds up under production traffic.
  • 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. This role is agent-focused and also includes some fullstack development.
  • Build agents only Honeycomb can build. Use a data store that returns high-cardinality queries in seconds to reason over signal a conventional backend can't serve at this fidelity correlating across services, drilling into a single trace, comparing before and after a deploy.
  • Extend the surface, and decide what's next. Ship new capability into Canvas, the MCP server, and Canvas Skills memory, spatial awareness, a faster Bedrock loop and make the case for what comes after with working code. Distinguish hype from signal in a field with plenty of both.
  • Define what "good" means for agents here. Set the bar: measurable against real evals, maintainable, and honest about their limits.

Requirements

What you’ll need
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Benefits

Comp & perks
  • A stake in our success - generous equity with employee-friendly stock program
  • It’s not about how strong of a negotiator you are - our pay is based on transparent levels relative to experience
  • Time to recharge with unlimited PTO
  • A distributed-first mindset and culture (really!)
  • 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...