The opportunity
The Privacy Engineering Team at OpenAI is committed to integrating privacy as a foundational element in OpenAI's mission of advancing Artificial General Intelligence (AGI). Our focus is on all OpenAI products and systems handling user data, striving to uphold the highest standards of data privacy and security.
What you'll do
Design and prototype privacy-preserving machine-learning algorithms (e.g.,: differential privacy, secure aggregation, federated learning) that can be deployed at OpenAI scale.
Measure and strengthen model robustness against privacy attacks such as: membership inference, model inversion, and data memorization leaks—balancing utility with provable guarantees.
Develop internal libraries, evaluation suites, and documentation that make: cutting-edge privacy techniques accessible to engineering and research teams.
Lead deep-dive investigations into the privacy–performance trade-offs of: large models, publishing insights that inform model-training and product-safety decisions.
Define and codify privacy standards, threat models, and audit procedures that: guide the entire ML lifecycle—from dataset curation to post-deployment monitoring.
Collaborate across Security, Policy, Product, and Legal to translate evolving: regulatory requirements into practical technical safeguards and tooling.
What they're looking for
- 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.
