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.