Research Engineer, Privacy and Anonymization

About this role

Build privacy and anonymization systems that protect sensitive data while preserving its utility for AI training. You'll design and deploy detection pipelines for PII and credentials, develop evaluation frameworks for privacy risk, and ensure robustness across diverse data sources in a production AI infrastructure environment.

What you'll do

• Design systems to detect PII, quasi-identifiers, credentials, and sensitive information with appropriate transformations
• Develop and benchmark detection approaches combining rules, statistical models, classifiers, and LLM-based methods
• Build production anonymization pipelines that process raw data before training, evaluation, and synthetic data generation
• Create evaluation frameworks measuring privacy risk and data utility, including leakage tests and re-identification attempts
• Engineer systems robust to schema drift, unusual formats, and sensitive information in unexpected locations
• Partner with engineering, research, and customers to translate privacy requirements into technical policies

What they're looking for

• Python and production data/ML systems development
• Information extraction, named-entity recognition, and classification
• Redaction, masking, pseudonymization, and anonymization techniques
• Data pipeline design and schema drift handling
• Privacy-enhancing technologies (differential privacy, k-anonymity, encryption)
• Experimental design and comparative evaluation methods
• Low-latency ML inference and high-throughput systems
• Privacy-sensitive domains (healthcare, finance, security)