Honeycomb currently has 2 active AI-related roles in our index. The most common open titles are: Senior Software Engineer - AI Intelligence, Senior Software Engineer - LLM Observability. Most positions are in Engineering.
Honeycomb's active AI hiring is concentrated in: agents (100%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Honeycomb is hiring AI talent in: United States (1 role), Canada (1 role).
Job postings at Honeycomb most frequently reference: llm observability, agent orchestration, model serving, rag, evals.
In the past 30 days, Honeycomb has posted 2 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Staff Solution Architect Staff Solution Architect role focused on driving technical strategy for large enterprise deals, advising clients on AI-era observability and modern operations, and leveraging AI tools to enhance efficiency. The role involves leading discovery, solution design, POCs, and influencing product direction. | — | 5 |
| Senior Solution Architect This role is for a Senior Solution Architect at Honeycomb, a company focused on observability tools. The primary responsibility is to drive technical sales motions for mid-market to enterprise accounts, leading discovery, solution design, and technical conversations. The role involves defining success criteria, running POCs, and engaging with engineering leaders on observability best practices and AI-era workflows. While the role mentions leveraging AI tools and AI-assisted workflows, the core function is solution architecture and technical sales, not direct AI/ML model development or research. The company is in the enterprise software space, and the role focuses on how their observability platform integrates with and supports modern engineering practices, including those involving AI. |
| — |
| 5 |
| Senior Software Engineer - LLM Observability Senior Software Engineer focused on building the foundational layer for LLM observability within Honeycomb. This role involves designing and implementing backend systems and APIs in Go, contributing to full-stack features with React/TypeScript, and providing technical leadership. The core responsibility is to make AI workloads observable by defining how AI systems are represented in telemetry, normalizing data from various ecosystems into a consistent OpenTelemetry-based model, and owning the infrastructure and data model for downstream observability experiences. The role emphasizes ownership, collaboration, and technical leadership in a distributed environment. | Agent | 5 |