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Salary: USD 225,000 - 265,000 / annual
Honeycomb is a Series D observability platform serving companies like Slack, HelloFresh, LaunchDarkly, and Vanguard. The company is 200+ people, fully distributed, and was named to Forbes' America's Best Startups in 2022 and 2023.
You will join the Data team as a senior individual contributor focused on operationalizing AI across the business. The core mission: take workflows that are slow, manual, or inconsistent and make them fast, automated, and measurably better—then systematize what you learn so the next workflow is easier to build.
Key responsibilities:
**Ship automated workflows end to end.** Partner with department leaders (RevOps, Customer Success, Sales, Product) to identify high-leverage workflows. Go deep on how work actually happens to know where AI helps and where it doesn't. Define success metrics before building (cycle time, quality, cost, risk), instrument them, and report honestly—including when something doesn't work. Deliver production systems with evaluation harnesses, human-in-the-loop review where stakes are high, and ongoing monitoring. Iterate on adoption like a product manager: user research, documentation, and a real plan for getting people to use it.
**Build the shared substrate as you go.** Establish patterns for model access, orchestration, agents, prompt and tool management, evaluation, logging, and cost control—derived from real workflows, not designed in advance. Build on the Data team's existing infrastructure rather than standing up parallel systems. Co-own shared components with Engineering; identify dependencies early and bring them into partner roadmaps.
**Make the company fluent in AI.** Create templates, playbooks, and golden paths (prompt patterns, agent patterns, safety checklists, evaluation patterns). Run office hours, demos, and community-of-practice sessions. Develop enablement material for distinct audiences—GTM, operations, leadership—from fundamentals through advanced workflows. Help people build judgment about when to use AI, when not to, and how to tell if it worked.
**Keep it safe and governable.** Partner with Security, IT, and Legal on access control, data handling, vendor risk, and policy. Apply data classification standards to AI use cases: what data can flow through LLM pipelines and what audit trails are required for sensitive data.
You sit at the seam between business and systems. The Data team already partners daily with RevOps, Customer Success, Sales, and Product, and owns the data infrastructure these workflows depend on. AI-in-the-business is fundamentally a data problem with a model attached.
**Requirements:**
- Deep experience shipping systems into real business workflows—you've owned something end to end, in production, that non-technical people depended on daily.
- Hands-on fluency with modern AI stacks: LLMs, retrieval, tool use and agents, evaluation, guardrails—plus the pragmatism to keep things shippable.
- Ability to learn someone else's job well enough to improve it. This role lives or dies on understanding how support, sales, or finance actually work.
- Track record of leading cross-functional initiatives without formal authority, bringing clarity, momentum, and crisp decision-making.
- Comfort navigating security, privacy, and compliance tradeoffs in real organizations: data classification, retention, access controls, vendor risk.
- Strong teaching instincts—you explain complex things simply, write clear docs, and meet people where they are.
- Demonstrated ability to stay current as the landscape shifts and to change your mind in public when evidence warrants it.
**Nice to have:**
- SQL fluency and comfort in a modern data stack (dbt, cloud warehouse, BI tooling).
- Experience with workflow automation or enterprise systems: case management, ticketing, CRM, knowledge bases.
- Familiarity with observability practices for AI systems: monitoring, quality signals, evaluation pipelines, cost and latency analysis.
- Change management experience across a distributed, multi-function company.
- Interest in external evangelism—writing, speaking, podcasts, peer groups.
