Research Engineer, Privacy

Highlights

• Build production privacy services safeguarding user data across OpenAI systems.
• Prototype privacy-preserving ML algorithms deployable at OpenAI scale.
• Investigate privacy-performance trade-offs to guide safe model training.

What they offer

• Relocation assistance
• Equity

Ideal candidate

Privacy-minded researcher who loves turning cutting-edge ideas into reliable systems and explaining complex risks clearly. You thrive bridging academia and production in fast-moving, mission-driven teams.

Requirements

• Have hands-on research or production experience with PETs
• Are fluent in modern deep-learning stacks (PyTorch/JAX) and comfortable turning cutting-edge papers into reliable, well-tested code
• Enjoy stress-testing models — probing them for private data leakage — and can explain complex attack vectors to non-experts with clarity
• Have a track record of publishing (or implementing) novel privacy or security work and relish bridging the gap between academia and real-world systems
• Thrive in fast-moving, cross-disciplinary environments where you alternate between open-ended research and shipping production features under tight deadlines
• Communicate crisply, document rigorously, and care deeply about building AI systems that respect user privacy while pushing the frontiers of capability