Engineering Manager, Distillation & Dectection Platform
Leads engineering team building scalable detection and mitigation systems to prevent misuse of frontier AI models, focusing on distillation detection and IP protection. Requires strong experience in adversarial environments, distributed systems, and cross-functional partnerships with Research and Product.
About the job
Responsibilities
- 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.
Requirements
- Experience building systems in adversarial, fast-evolving environments.
- Comfortable with ambiguity and novelty.
- 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.
Nice-to-Haves
- Background in autonomy, high-scale real-time systems, or intelligence-adjacent technical domains.
Skills
Distributed Systems, Data Pipelines, Observability, System Design, Applied Ml, Backend Engineering, Production Services, Monitoring, Automation, Ml Engineering
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