Privacy ML Engineer: Scalable, Privacy-Preserving AI

OpenAI; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

OpenAI is seeking a Privacy Engineer in San Francisco to advance privacy-preserving machine learning techniques across our AI systems. The role focuses on differential privacy, federated learning, and data anonymization while balancing data utility with strong privacy guarantees.

You will design scalable privacy methods, develop evaluation tools, and publish insights to guide model training and production deployment. Relocation assistance is available.

Qualifications

• Experience designing privacy-preserving ML algorithms for large-scale systems.
• Strong understanding of differential privacy and related techniques.
• Familiarity with federated learning and data anonymization methods.
• Knowledge of attacks such as membership inference and model inversion.

Responsibilities

• Design privacy-preserving ML algorithms deployable at OpenAI scale.
• Measure robustness against privacy attacks while balancing utility and guarantees.
• Develop internal libraries and evaluation tools for privacy features.
• Lead investigations into privacy–performance trade-offs for large models.

Skills

Privacy-aware ML

Differential privacy

Federated learning

Data anonymization

Privacy attacks knowledge

Tools

Secure aggregation framework

Evaluation suites

Job description

OpenAI is seeking a Privacy Engineer in San Francisco to advance privacy-preserving machine learning techniques across our AI systems. The role focuses on differential privacy, federated learning, and data anonymization while balancing data utility with strong privacy guarantees.

You will design scalable privacy methods, develop evaluation tools, and publish insights to guide model training and production deployment. Relocation assistance is available.