As AI agents increasingly write and operate infrastructure, reliability systems must be able to reason and act at machine speed. In this role, you will set the technical direction and drive execution for an AI agent that autonomously gathers context, investigates incidents, identifies likely root causes, builds a safe mitigation plan, and executes it - with humans supervising and able to intervene at any point.
The immediate challenge is building an agent that is both highly accurate and broadly trusted. Experienced incident responders need a transparent collaborator that helps them investigate, continuously surfaces relevant evidence, and keeps them in control. Engineers who encounter incidents only occasionally need an agent that leads the investigation and guides them to a safe outcome.
You will lead two tightly connected areas
- Agent quality - skills, tools, context, memory, offline evaluation, backtesting, online experimentation, and learning from production outcomes.
- Responder experience - a multi-turn interface that gives responders visibility into the agent's work and brings together the operational data they need to make decisions.
This is a deeply hands-on technical leadership role at the intersection of large-scale distributed systems, incident response, applied AI, and product engineering. You will personally design and ship critical parts of the system while leading senior engineers across team and organizational boundaries. The long-term opportunity is to help create self-healing infrastructure that resolves production incidents safely at Meta scale.
