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.

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.

Benefits

Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
401(k) retirement plan with employer match
Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
Mental health and wellness support
Employer-paid basic life and disability coverage
Annual learning and development stipend to fuel your professional growth
Daily meals in our offices, and meal delivery credits as eligible
Relocation support for eligible employees
Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdifferential privacysecure aggregationfederated learningmodel robustnessprivacy attacksevaluation suitesprivacy-preserving techniquesdeep learningPyTorch

Soft Skills

communicationcollaborationproblem-solvingresearchdocumentationclarityadaptabilitycross-disciplinary teamworktime managementcritical thinking