Senior Software Engineer II - Agentic Intelligence

Description

What We’re Building

Honeycomb is a service for the near and present future, defining observability and raising expectations of what developer tools can do! We’re working with well known companies like HelloFresh, Slack, LaunchDarkly, and Vanguard and more across a range of industries. This is an exciting time in our trajectory, we’ve closed Series D funding, scaled past the 200-person mark, and were named to Forbes’ America’s Best Startups of 2022 and 2023!

If you want to see what we’ve been up to, please check out these blog posts and Honeycomb.io press releases .

Who We Are

We come for the impact, and stay for the culture! We’re a talented, opinionated, passionate, fiercely inclusive, and responsible group of bees. We have conviction and we strive to live our values every day. We want our people to do what they truly love amongst a team of highly talented (but humble) peers.

Responsibilities

• 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.
• Example projects
• Multiple agents collaborating on one shared Canvas investigation each claiming a hypothesis, publishing findings, and narrowing the search space for the others so it resolves faster (blog).
• Auto-investigation the moment an SLO burn alert fires the agent forms hypotheses and prepares visualizations before a human looks, cutting mean-time-to-insight for on-call (o11ycon 2026).
• Skills that encode a team's domain expertise e.g. Kubernetes thresholds so agents and human colleagues can lean on them (o11ycon 2026).
• What you'll bring:
• 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.
• Even better
• 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.
• Base Salary based on level of experience
• $250,000 - $280,000 CAD
• What you'll get when you join the Hive:
• 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...
• Please note we cannot currently sponsor or support visa transfers at this time. Additionally, in compliance with applicable law, all persons hired will be required to verify identity and eligibility to work.
• Phishing and Recruitment Scam Warning:
• We take your security seriously. Please be aware that recruitment scams are increasingly common and scammers may create email addresses or websites to impersonate Honeycomb employees. To help protect you:
• All communications will come from an @honeycomb.io email address
• We occasionally work with external recruiting agencies. These partners will use legitimate business email addresses— never personal accounts like Gmail or Yahoo .
• Our recruiting process will never ask you to provide financial or sensitive personal information, including but not limited to:
• Social security or tax identification numbers
• Credit card numbers
• Bank account information

Conditions

We are a fully distributed company, which means we believe it is not where you sit, but how you deliver that matters most. We invest in our people and care about how you orient to our culture and processes. At the same time we imbue a lot of trust, autonomy, and accountability from Day 1. LI-Remote

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. 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 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.