Honeycomb.io - Staff AI Operations Lead
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. • 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 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 the evidence does. • SQL fluency and comfort in a modern data stack (dbt, a 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. • Base Salary based on level of experience • $225,000—$265,000 USD • What you'll get when you join the Hive: • A stake in our success - generous equity with employee-friendly stock program • It’s not about how strong of a negotiator you are - our pay is based on transparent levels relative to experience • Time to recharge with unlimited PTO • A distributed-first mindset and culture (really!) • Full benefits coverage for employees, with additional coverage available for dependents • Up to 16 weeks of paid parental leave, regardless of path to parenthood • Annual development allowance • And much more... • Please note we cannot currently sponsor or support visa transfers at this time. Additionally, in compliance with applicable law, all persons hired will be required to verify identity and eligibility to work. • Please note we cannot currently sponsor or support visa transfers at this time. • Phishing and Recruitment Scam Warning: • We take your security seriously. Please be aware that recruitment scams are increasingly common and scammers may create email addresses or websites to impersonate Honeycomb employees. To help protect you: • @honeycomb.io • We occasionally work with external recruiting agencies. These partners will use legitimate business email addresses—never personal accounts like Gmail or Yahoo. • never personal accounts like Gmail or Yahoo • never • Credit card numbers • Bank account information • Diversity & Accommodations: • We're committed to building a diverse, inclusive, and equitable workplace—where people of all backgrounds, identities, experiences, and abilities are welcomed, valued, and supported. We recognize that there is no single path to success and embrace nontraditional career journeys and diverse perspectives as key to building stronger, more innovative teams. • We strive to ensure an inclusive experience throughout every stage of our hiring process and are happy to provide reasonable accommodations as needed. If you require accommodations or accessible formats at any point during our hiring process, please let your recruiter know.
Responsibilities
• 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.
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