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Honeycomb.io

Observability platform for cloud applications

Senior Software Engineer 2 - Agent Observability

Full-Stack EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • Significant software engineering experience, with a record of shipping and owning features end to end.
  • Experience shipping customer-facing features at a SaaS company, beyond internal tooling.
  • Full-stack range, with proficiency in backend development using Go and/or Python and frontend development using TypeScript and React.
  • A track record of working closely with product managers and designers.
  • Regular, hands-on use of AI tools in day-to-day engineering work.
  • Experience being on call; the employee would join the rotation after about 90 days.
  • A track record of navigating ambiguity in a fast-moving field where requirements shift and experimentation is a core part of the job.

Responsibilities

  • 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 the product observe agents and their ecosystems across Honeycomb.
  • Build systems that analyze and surface AI-specific signals, such as cost, token usage, errors, and anomalies.
  • Collaborate with product and design partners to ensure the product is usable, intuitive, and supports meaningful user workflows.
  • Teach best practices for exploring and understanding AI system behavior across conversations and workloads.
  • Iterate through fast feedback loops and experimentation, and share both wins and failures.

Desired Qualifications

  • Experience building LLM or agent-powered features in production.
  • Hands-on experience with observability tooling, such as OpenTelemetry.

About the Company

Honeycomb.io

Observability platform for cloud applications

Honeycomb.io provides a cloud observability platform for cloud applications. It collects telemetry data from applications and lets users query billions of events to understand performance and behavior. It uses Service Level Objectives (SLOs) to alert when customer experiences degrade, enabling teams to quickly debug issues. It offers tools like Query Builder, Query Assistant, and BubbleUp to analyze data and compare subsets against baselines. Its pricing is likely subscription or usage-based, charging based on data sent for analysis. The company differentiates itself by handling high-cardinality event data at scale with fast, flexible querying and by focusing on problem diagnosis and reliability for a wide range of customers. The goal is to help teams identify, debug, and optimize cloud applications to improve reliability and customer experience.

Company size
201-500
Company stage
Series D
Total funding
$146.9M
Headquarters
San Francisco, CA
Founded
2016

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