Research Engineer, Privacy and Anonymization

About the Role

Join a research and engineering team building privacy and anonymization systems for sensitive data used in AI training. You will help protect personal and confidential information while preserving the structure and utility of data for downstream workflows.

What You'll Do

• Build systems to detect personally identifiable information, quasi-identifiers, credentials, and other sensitive content, then select transformations suited to the data and its intended use.
• Develop and benchmark detection methods that combine rules, statistical models, classifiers, and language models.
• Build production pipelines that anonymize raw data before it reaches processing, training, evaluation, or synthetic data workflows.
• Create evaluation frameworks for privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
• Design robust systems that handle new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
• Work with engineering, research, operations, and customers to turn privacy requirements into practical policies and safeguards.

What We're Looking For

• At least 2 years of experience building production data or machine learning systems in Python, with strong Python proficiency.
• A technical education or equivalent technical background, and experience building end-to-end data processing pipelines.
• Hands-on experience detecting, removing, or anonymizing PII while preserving useful characteristics, using information extraction, named-entity recognition, classification, or related methods.
• Experience benchmarking detection methods and evaluating recall, precision, latency, cost, and downstream data utility.
• Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
• Experience designing systems resilient to schema drift, unusual data formats, and processing edge cases.
• Experience with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption is valuable.
• Experience with high-throughput or low-latency inference, sensitive data in areas such as healthcare, finance, or security, or taking research systems into production is also valuable.