Research Engineer, Privacy and Anonymization

HUD is building infrastructure for reinforcement learning training data and evaluations for frontier AI agents. The Research Engineer will build privacy and anonymization systems to detect and transform sensitive information, develop production data-protection pipelines, and evaluate privacy risk while preserving data utility for AI training.

Responsibilities:

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on the data type and downstream use case
• Develop and benchmark detection approaches that combine rules, statistical models, classifiers, and LLM-based methods
• Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation, or synthetic data generation workflows
• Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts
• Design systems that remain robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields
• Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards