Senior Research Engineer, Privacy and Anonymization

Department: Engineering

Location: San Francisco

Employment Type: FullTime

About the Role

Build privacy and anonymization systems that help make sensitive real-world data safe and useful for AI training. You will develop end-to-end methods to protect sensitive information while preserving the structure and signal needed for downstream training, evaluation, and synthetic data workflows.

What You'll Do

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, and tailor transformations to data types and use cases.

• Develop and benchmark detection approaches that combine rules, statistical models, classifiers, and LLM-based methods.

• Create production pipelines that anonymize data before it enters processing, training, evaluation, or synthetic data workflows.

• Develop evaluation frameworks for privacy risk and retained utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.

• Design robust systems that handle new sources, schema drift, unusual formats, and sensitive information in unexpected fields.

• Partner with engineering, research, operations, and customers to turn privacy requirements into practical safeguards.

What We're Looking For

• At least 2 years of experience building production data or ML systems in Python, with strong proficiency in the language.

• Hands-on experience with PII detection, removal, or anonymization, including transformations that preserve useful data characteristics while hiding underlying information.

• Experience with information extraction, named-entity recognition, classification, or related methods for finding rare or sensitive content.

• Ability to build end-to-end data pipelines and compare approaches across recall, precision, latency, cost, and downstream utility.

• Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.

• Experience handling schema drift and edge cases; work with sensitive data or privacy-enhancing techniques such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption is valuable.

• Experience with low-latency or high-throughput ML inference and data processing is beneficial.

Compensation & Benefits

Salary range: $130,000 to $225,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California, United States.