Research Engineer, Privacy and Anonymization

12 hours ago

H1B sponsor

Responsibilities

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

At least 2 years of hands-on experience building production data or ML systems in Python.
Proficiency in Python and 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 sensitive-content detection methods.
Ability to build end-to-end data-processing pipelines without a fully prescribed roadmap.
Strong experimental judgment across recall, precision, latency, cost, and downstream data utility.
Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
Experience building systems robust to schema drift, unusual data formats, and edge cases.
Familiarity with 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.