Research Engineer, Privacy and Anonymization

About the Role

This Research Engineer role sits at the intersection of privacy engineering and AI infrastructure, owning the systems that make sensitive, real-world data safe for AI training. You will design and build end-to-end anonymization pipelines that protect privacy without sacrificing the structure and signal that make data valuable for training frontier AI agents. The work is high-impact: it directly gates what data can enter production training, evaluation, and synthetic data workflows.

What You'll Do

• 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.

What We're Looking For

• 2+ years of hands-on experience building production data or ML systems in Python.

• Proficiency in Python with a track record of building reliable, production-grade systems.

• Hands-on experience with PII detection, removal, or anonymization.

• Experience with information extraction, named-entity recognition, classification, or related methods for detecting sensitive or rare content.

• Proven ability to build end-to-end data processing pipelines without a fully prescribed roadmap.

• Strong experimental instincts: comfortable comparing approaches across recall, precision, latency, cost, and downstream data utility.

• Solid understanding of privacy transformation techniques: redaction, masking, pseudonymization, anonymization, and synthetic data generation.

• Experience designing systems that are robust to schema drift, unusual data formats, and edge cases.

• 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, or security domains is a plus.

Location

On-site in San Francisco, California, USA. Visa sponsorship is available.