Senior Software Engineer II – Agentic Intelligence

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Honeycomb.io

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

☁️ SaaS

🏢 Enterprise

🤖 Artificial Intelligence

SaaS • Enterprise • Artificial Intelligence

Honeycomb. io is an observability platform designed to provide comprehensive insights into application performance. It unifies logs, metrics, and traces into a single data type, allowing engineers to quickly diagnose and resolve issues. Honeycomb. io offers features like distributed tracing, anomaly detection, and service maps to help teams enhance system visibility and operational efficiency. It integrates with popular cloud services like Amazon Web Services and Kubernetes, and supports technologies such as OpenTelemetry. Honeycomb. io aims to enable engineering teams to deploy confidently, reduce incident response times, and improve overall productivity.

📋 Description

• 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

• 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

• 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...