S Staff AI Operations Lead Remote - Canada, CA Principal Go SQL LLM

ContentBuffer; Canada; Remote; CAD 120,000 - 180,000; Full time

Job summary

Honeycomb is a distributed company shaping observability with AI-powered workflows. You’ll sit on the Data team, partnering with RevOps, Customer Success, Sales, and Product to embed data-driven automation across the company.

As a senior individual contributor, you’ll build end-to-end workflows, optimize models in production, and foster adoption with clear governance and measurement. We value autonomy, accountability, and a bias toward shipping: you’ll deliver working systems, monitor outcomes,

Qualifications

• Deep experience shipping systems into real business workflows.
• Hands-on fluency with modern AI stacks: LLMs, retrieval, tool use and agents, evaluation, guardrails.
• The ability to learn someone else’s job well enough to improve it.
• A track record of leading cross-functional initiatives without formal authority.

Responsibilities

• Ship automated workflows end to end by partnering with department leaders to identify high-leverage workflows and understand how the work actually happens to know where a model helps and where it doesn’t.
• Define success before you build: cycle time, quality, cost, risk. Instrument it and report honestly, including when something didn’t work.
• Deliver working systems, not prototypes, with evaluation harnesses, human-in-the-loop reviews where required, and ongoing monitoring.
• Iterate on adoption like a product manager: user research, documentation, and a plan to get people to use the thing.
• Build the shared substrate by establishing patterns for model access, orchestration, and cost control, reusing existing Data team infrastructure.

Job description

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 liveour valuesevery 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

About the problem space

AI has changed what's possible in go-to-market, support, and business operations. The constraint is no longer \"can we build it\" — it’s \"can we deploy it into a real workflow, prove it made things better, and keep it working.\"

Most companies are stuck at the demo stage: a lot of clever prompting, very little that runs reliably on Tuesday morning without someone watching it. This role exists to get us past that, everywhere outside of engineering.

Little more about the team:

You’ll sit on the Data team, which is where this work belongs. Our team already operates at the seam between the business and its systems — we partner daily with RevOps, Customer Success, Sales, and Product, and we own the data infrastructure these workflows depend on. AI-in-the-business is a data problem with a model attached, and you’ll have the pipelines, the warehouse, and the domain relationships already in place.

What you’ll do

You are a senior individual contributor. Your job is to take business workflows that are slow, manual, or inconsistent, and make them fast, automated, and measurably better — then do it again, and leave behind enough shared scaffolding that the third one is easier than the first.

Ship automated workflows end to end

• Partner with department leaders to identify the highest-leverage workflows in their orgs, and go deep enough on how the work actually happens to know where a model helps and where it doesn’t.


• Define success before you build: cycle time, quality, cost, risk. Instrument it. Report on it honestly, including when the answer is \"this didn’t work.\"


• Deliver working systems, not prototypes, with evaluation harnesses, human-in-the-loop review where the stakes call for it, and ongoing monitoring.


• Iterate on adoption the way a product manager would: user research, documentation, and a real plan for getting people to use the thing.

Build the shared substrate as you go

• Establish patterns for model access, orchestration and agents, prompt and tool management, evaluation, logging, and cost control — derived from what real workflows needed, not designed in advance.


• Build on the Data team’s existing infrastructure rather than standing up parallel systems.


• Co‑own shared components with your counterpart in Engineering; identify dependencies early and bring them into partner roadmaps collaboratively.

Make the company fluent

• Create templates, playbooks, and golden paths: prompt patterns, agent patterns, safety checklists, evaluation patterns.


• Run office hours, demos, and community‑of‑practice sessions that make adoption feel accessible rather than intimidating.


• Develop enablement material for distinct audiences — GTM, operations, leadership — from fundamentals through advanced workflows.


• Help people build judgment about AI: when to use it, when not to, and how to tell whether it worked.

Keep it safe and governable

• Partner with Security, IT, and Legal on access control, data handling, vendor risk, and policy — so that employees have a clear, fast, safe path to experiment.


• Work with the Data team to apply our data classification standards to AI use cases: what data can be used for retrieval and context, and what audit trails are required when sensitive data flows through LLM pipelines.

What you’ll bring

• 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.


• The 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.


• A track record of leading cross‑functional initiatives without formal authority, bringing clarity, momentum, and crisp decision‑making.


• Comfort navigating s