Senior Software Engineer II - LLM Observability

About this role

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. How We Work 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: The team builds observability for AI in Honeycomb. We help customers see what their agents are doing, what those agents cost, and where errors happen. We also give them insight into individual conversations so they can debug them.

We're bringing LLM and agent observability together, and we own both the data foundation and the product experiences built on top of it. You'll work in a small, cross-functional group of engineers with a dedicated product manager and designer, shipping features customers use in production. If you want to define how people understand and debug AI systems, this is the team for you. What you’ll do in the role: Own end-to-end development of AI observability capabilities, from ingestion to in-product visualizations.

Architect and evolve the data model and infrastructure that let us observe agents and their ecosystems across the Honeycomb product. Build systems that analyze and surface AI-specific signals, such as cost, token usage, errors, and anomalies. Collaborate with product and design partners to make sure what we build is usable, intuitive, and unlocks meaningful user workflows. Teach best practices for exploring and understanding AI system behavior across conversations and workloads.