Research Engineer, Privacy and Anonymization

CLERA; San Francisco (CA); Presencial; USD 130.000 - 225.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Visa sponsorship available

Descripción de la vacante

Clera Labs, Inc. is hiring for a Privacy Engineer to build and productionize privacy-preserving data systems for AI workflows in San Francisco. The role focuses on detecting sensitive information, applying transformations, and evaluating privacy vs. utility across diverse data sources.

You will develop end-to-end data pipelines, benchmark approaches, and collaborate with engineering and customers to implement practical safeguards and policies ensuring compliant, useful model training data.

Formación

• 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; privacy-enhancing technologies or sensitive data in healthcare, finance, or security is valuable.

Responsabilidades

• 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.

Conocimientos

Python

Data pipelines

ML systems

Educación

Bachelor's degree or equivalent

Descripción del empleo

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.

What You'll Do

• 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.

What We're Looking For

• 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.

Compensation & Benefits

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

Location

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