Research Engineer, Privacy and Anonymization

About the role

This role focuses on building privacy and anonymization systems that keep sensitive real-world data safe and useful for AI training. You will own the end-to-end pipeline, bridging applied research and production engineering in a fast-moving AI infrastructure company.

Key responsibilities

• Design and implement detection systems for PII, quasi-identifiers, credentials, and other sensitive content, choosing transformations based on data type and downstream use case.
• Develop and benchmark detection approaches that combine rule-based methods, statistical models, classifiers, and LLM-based techniques.
• Build production pipelines that anonymize raw data before it enters training, evaluation, or synthetic data generation workflows.
• Create evaluation frameworks measuring privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
• Ensure systems remain robust to new data sources, schema drift, unusual formats, and hidden sensitive fields.
• Collaborate with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards.

Required profile

• 2+ years building production data or ML systems in Python.
• Hands-on experience with information extraction, named-entity recognition, classification, or related methods for detecting sensitive content.
• Strong experimental instincts with ability to compare approaches across recall, precision, latency, cost, and downstream utility.
• Experience designing systems that handle schema drift, unusual formats, and edge cases.
• End-to-end experience building data processing pipelines without a fully prescribed roadmap.

Required skills

• Python programming.
• Redaction, masking, pseudonymization, anonymization, and synthetic data generation techniques.
• Familiarity with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption.
• Knowledge of low-latency or high-throughput ML inference and data-processing systems (optional but beneficial).