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Salary: CAD 310,000 - 360,000 / annual
Honeycomb is a Series D observability platform used by companies like Slack, HelloFresh, LaunchDarkly, and Vanguard. The company is fully distributed and has scaled past 200 employees.
You will join the Data team as a senior individual contributor focused on operationalizing AI across the business. The core challenge: most companies are stuck at the demo stage with AI—clever prototypes that don't run reliably in production. This role exists to move Honeycomb past that constraint, deploying AI into real workflows outside of engineering (GTM, support, operations, finance).
Your responsibilities:
**Ship automated workflows end to end.** Partner with department leaders to identify high-leverage manual or slow workflows. Go deep on how work actually happens to understand where AI helps and where it doesn't. Define success metrics upfront (cycle time, quality, cost, risk) and instrument them. 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 would: user research, documentation, and a real adoption plan.
**Build the shared substrate as you go.** Establish patterns for model access, orchestration, agents, prompt management, evaluation, logging, and cost control—derived from real workflows, not designed in advance. Build on the Data team's existing infrastructure rather than creating parallel systems. Co-own shared components with Engineering and bring dependencies into partner roadmaps collaboratively.
**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 GTM, operations, and leadership audiences. Help people build judgment about when to use AI, when not to, and how to measure whether 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. You already have pipelines, a warehouse, and domain relationships with RevOps, Customer Success, Sales, and Product in place.
**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 pragmatism to keep things shippable.
- Ability to learn someone else's job well enough to improve it. This role lives 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: 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.
