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About the Role
Build privacy and anonymization systems for an AI infrastructure team, helping make sensitive real-world data safe and useful for model training. You will own production methods that protect private information while preserving the structure and signal needed for training and evaluation.
Responsibilities
- Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, and choose 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 raw data before downstream processing, training, evaluation, or synthetic data generation.
- Build evaluation frameworks for privacy risk and retained utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
- Design robust systems for new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
- Partner with engineering, research, operations, and customers to turn privacy requirements into practical policies and safeguards.
Requirements
- At least 2 years of experience building production data or ML systems in Python.
- Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.
- Experience with information extraction, named-entity recognition, classification, or related techniques for detecting sensitive or rare content.
- 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.
- Strong experimental skills and experience handling schema drift, unusual formats, and edge cases.
- Technical education or equivalent background; experience with privacy-enhancing technologies or sensitive data in healthcare, finance, or security is valuable.
Skills
- Python
- Information extraction
- Named-entity recognition
- Classification
- LLM-based approaches
- Privacy-enhancing technologies
Location
- San Francisco, California, United States
Work Type
- On-site
- Full-time
Experience Level
- Mid-level
Education Level
- Technical degree or equivalent background
Salary/Compensations
- $130,000 to $225,000 annually
Benefits
- Visa sponsorship