Research Engineer, Privacy and Anonymization

Department: Engineering

Location: San Francisco

Employment Type: FullTime

About the Role

Build privacy and anonymization systems for an AI infrastructure team, helping make sensitive real-world data safer to use in model training and evaluation. You will develop end-to-end methods that protect sensitive information while preserving the structure and utility of data.

What You'll Do

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, then choose transformations suited to the data and its downstream use.

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

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

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

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

• Collaborate across engineering, research, operations, and customer-facing teams to turn privacy requirements into practical safeguards.

What We're Looking For

• At least 2 years of experience building production data or ML systems with strong Python skills.

• Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.

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

• Experience building end-to-end data processing pipelines and working through open-ended problems from research to production.

• Ability to compare methods across recall, precision, latency, cost, and downstream data utility.

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

• Experience with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption is valuable.

• Experience with sensitive data in healthcare, finance, security, or related fields, and with high-throughput or low-latency ML systems, is a plus.

Compensation & Benefits

Base salary: $130,000 to $225,000 USD annually. Visa sponsorship is available.

Location

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