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Research Engineer, Privacy and Anonymization

Clera

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Skills

Required

  • anonymization
  • pipelines
  • synthetic data generation
  • schema drift
  • ML
  • Python
  • information extraction
  • named-entity recognition
  • classification
  • redaction
  • masking
  • pseudonymization
  • edge cases

Preferred

  • training
  • statistical models
  • classifiers
  • LLM-based
  • leakage tests

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.

Preferred

  • Technical education or equivalent background; experience with privacy-enhancing technologies or sensitive data in healthcare, finance, or security is valuable.

Pay

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

Location

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