SF On-Site Research Engineer, Privacy & Anonymization

Clera; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Clera in San Francisco, CA is seeking a Research Engineer focused on building privacy and anonymization systems to make sensitive data safe for AI training. You will own the full pipeline for protecting privacy without losing data value, bridging applied research and production engineering.

You will detect PII and sensitive info, develop robust anonymization workflows, and build evaluation tools to balance privacy with utility, collaborating across teams and with customers.

Qualifications

• 2+ years building production data or ML systems in Python.
• Hands-on experience with information extraction, named-entity recognition, classification, or related methods for detecting sensitive or rare content.
• Strong experimental instincts and the ability to compare approaches across recall, precision, latency, cost, and downstream data utility.
• Solid understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation, and when each technique is appropriate.
• Experience designing systems that are robust to schema drift, unusual data formats, and edge cases.
• End-to-end experience building data processing pipelines without a fully prescribed roadmap.
• Familiarity with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption is a plus.
• Experience with low-latency or high-throughput ML inference and data-processing systems is a plus.
• Prior work with sensitive data in healthcare, finance, security, or related domains is a plus.

Responsibilities

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, designing transformations based on data type and downstream use case.
• Develop and benchmark detection approaches that combine rules, statistical models, classifiers, and LLM-based methods.
• Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation, or synthetic data generation workflows.
• Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
• Design systems that remain robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields.
• Collaborate with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards.

Skills

Python

Information extraction

NER

Experimentation

Data pipelines

Job description

Clera in San Francisco, CA is seeking a Research Engineer focused on building privacy and anonymization systems to make sensitive data safe for AI training. You will own the full pipeline for protecting privacy without losing data value, bridging applied research and production engineering.

You will detect PII and sensitive info, develop robust anonymization workflows, and build evaluation tools to balance privacy with utility, collaborating across teams and with customers.