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    Research Engineer, Privacy and Anonymization

    CleraOn-site - San Francisco, California, United StatesPosted 2026-10-28
    This listing's locations
    On-site - San Francisco, California, United States
    Employment
    Full-time
    Workplace
    On site
    Compensation
    130000–225000 per year
    Department: Engineering

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    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.