Join a team building privacy and anonymization systems for sensitive real-world data used in AI training. You will develop methods that protect personal and confidential information while preserving the structure and signal that make data useful.
Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, then select transformations suited to each data type and use case.
Develop and benchmark detection methods combining rules, statistical models, classifiers, and LLM-based approaches.
Create production pipelines that anonymize data before downstream processing, training, evaluation, or synthetic data generation.
Build evaluation frameworks for privacy risk and retained data utility, including 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 safeguards.
At least 2 years of experience building production data or ML systems in Python, with hands-on experience transforming PII while preserving useful data characteristics.
Experience with PII detection, removal, or anonymization, and with information extraction, named-entity recognition, classification, or related techniques.
Experience building end-to-end data processing pipelines and benchmarking approaches across recall, precision, latency, cost, and 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.
Strong experimental judgment and attention to edge cases; experience with sensitive data or high-throughput ML and data systems is valuable.
Base salary: $130,000 to $225,000 annually. Visa sponsorship is available.
On-site in San Francisco, California, United States.