Head of AI Native Operations

About Supabase Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth.

We are looking for a Head of AI Native Operations to build the autonomous systems that run Supabase. Supabase is building an AI-native internal operating system, where agents carry the operational load end to end. We are standing up a new central team to build that platform and run it in production. This is the role that leads it, and it is hands-on. You design the architecture, build the first versions of the critical pieces, and are accountable for whether every agent we run is correct, safe, and improving over time. We build rather than buy, and there are no settled best practices for most of this yet, so you will be setting them. You will lead a team of senior individual contributors extending the platform and driving adoption across teams. What You’ll Be Responsible For In this role, you’ll: Own the agent platform architecture: The harness and orchestration layer and connectors into company systems, shared context and memory, and the path from prototype to production. Catch regressions before they ship: Model, prompt, and tool changes can’t ship until they pass the evals. When a new model comes out, you know within hours whether to adopt it. Make every agent observable: Every run and every decision is traced. For any agent at any time, you can answer what it did, why, what it cost, and how its quality is trending. Own safety and trust: Design the controls that let agents act without a human in the loop: least-privilege credentials, hard policy limits, escalation thresholds, audit logs, sandboxing, and prompt-injection defenses. Replace workflows, don’t assist them: Pick the workflows agents can fully own today and take them to production. Turn wins into a platform: Package what works into reusable components, templates, and eval harnesses so other teams can ship their own production agents without starting from scratch. Build and direct the team: Hire and grow senior ICs, review their designs and pull requests, and hold the quality bar. Measure what matters: Track workflows automated, eval pass rates, cost per run, and work absorbed against the hiring plan. How You’ll Think A Point of View on Where Automation Wins You have a thesis about how AI and automation change the way a company operates, and it comes from building, not reading. You can look at a business and say which workflows agents can own today, which they will own in 12 months as models improve, and which still need people, and why. You defend that view with evidence from systems you have shipped, and you update it when the evals tell you to. Eval-Driven You don’t trust an agent because the demo looked good. You trust it because it passes a test set of real cases, and you know which cases it fails. When anything changes, your first question is “what do the evals say?” Inversion Thinking Left alone, an autonomous system tends to fail quietly, or people route around it. So the first question about any agent isn’t “does it work on the happy path?” but “how will this break, and how will we know?” The design follows from that answer: what the agent is never allowed to do, what it escalates, and which alert fires when quality drifts. Recursive Thinking Every system you build should produce the evidence that grades it. An agent isn’t done when it works. It’s done when it tells you, without you in the loop, that it has stopped working. Must Have Hands-on agent building: Have personally designed and built autonomous agent or LLM systems that ran in production and that others depended on. Prototypes, demos, and configuring a vendor tool don’t count. You don’t need to be a career software engineer, but you must have built these systems yourself.