Engineering Manager, Preparedness

OpenAI•San Francisco, CA

6d•$445,000 - $510,000•Hybrid

About The Position

The Preparedness Engineering team builds and operates safeguards for frontier model risks, enabling advanced capabilities while detecting and limiting harm. The Engineering Manager will lead engineers building production software systems—including detection pipelines and mitigation mechanisms—to prevent harmful misuse of frontier AI models and protect model integrity. This is a builder-oriented role focused on reliable systems that can operate at billions-of-users scale. The position is based in San Francisco, California, with a hybrid schedule of three days in the office per week. Relocation assistance is offered to new employees. The manager will set the technical roadmap and execution strategy, partner with Research and Product to develop scalable tests and mitigations, uphold strong engineering fundamentals, and hire and develop an exceptional engineering team.

Requirements

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, and Security to ship systems that materially reduce risk or abuse at scale.
Experience building systems in adversarial, fast-evolving environments.
Ability to thrive in high-context environments and work with ambiguity and novelty.
Clear communication and ability to build trust quickly with senior stakeholders; pragmatic, collaborative, and calm under scrutiny.

Nice To Haves

Background in autonomy, high-scale real-time systems, or intelligence-adjacent technical domains.

Responsibilities

Lead a team of software engineers building detection and mitigation systems for frontier model harms.
Set the technical roadmap and execution strategy, including prioritization, design, shipping, iteration, and impact measurement.
Build production services, pipelines, tooling, instrumentation, and automation that scale with frontier model usage.
Partner with Research and Product to translate evolving model capabilities into tests, signals, and mitigations deployable at scale.
Drive engineering fundamentals in architecture, reliability, monitoring, performance, and operational excellence.
Hire and grow a team across backend, data systems, and applied machine learning engineering as needed.
Anticipate scaling challenges as agentic workflows become more capable.