About this role
About the Role
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.
What You'll Do
- 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.
What We're Looking For
- 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.
Compensation & Benefits
Base salary: $130,000 to $225,000 annually. Visa sponsorship is available.
Location
On-site in San Francisco, California, United States.