Engineering Manager, Distillation & Detection Platform

About the role

We’re looking for an engineering manager to lead a team building software systems that detect and prevent harmful misuse of frontier AI models-before incidents occur. This is a builder’s role: you’ll lead engineers shipping production services, detection pipelines, and mitigation mechanisms that protect frontier model integrity and reduce high-severity misuse risk.

While this work intersects with frontier model development, security and risk, we’re explicitly seeking someone with a software engineering foundation who is comfortable building reliable systems that can operate at billions of users scale.

In this role you will

• Lead a team of software engineers building detection + mitigation systems for frontier model misuse, with an emphasis on model IP protection / distillation detection and emerging risk surfaces from autonomous agents.
• Set the technical roadmap and execution strategy: prioritize, design, ship, iterate, measure impact.
• Build production systems: services, pipelines, tooling, instrumentation, and automation that scale with frontier model usage.
• Partner deeply with Research and Product to translate evolving model capabilities into concrete tests, signals, and mitigations that can be deployed at scale.
• Drive strong engineering fundamentals: architecture, reliability, monitoring, performance, and operational excellence.
• Hire and grow an exceptional team across backend, data systems, and applied ML engineering domains as needed.
• Anticipate what breaks at scale as agentic workflows become more capable.

You might thrive in this role if you

• Experience building systems in adversarial, fast-evolving environments
• Are comfortable with ambiguity and novelty
• Have experience adjacent to security (e.g., abuse prevention, fraud, integrity, platform defense, auth/identity, malware/spam, adversarial environments)
• Communicate clearly and build trust quickly with senior stakeholders-pragmatic, collaborative, and calm under scrutiny.
• Significant experience leading engineering teams and delivering production systems end-to-end.
• Strong technical judgment in system design, distributed systems, data pipelines, observability, and operational reliability.
• Demonstrated ability to partner cross-functionally with Research/Product/Security to ship systems that materially reduce risk or abuse at scale.
• Familiarity with model extraction / distillation, adversarial evaluation, or scalable detection/mitigation approaches.
• Background in autonomy, high-scale real-time systems, or intelligence-adjacent technical domains is a plus.