Research Engineer, Privacy and Anonymization

3 days ago

H1B sponsor

Base Salary

$130k - $225k/yr

Responsibilities

Build systems to detect PII, quasi-identifiers, credentials, and other sensitive content and select suitable transformations.
Develop and benchmark detection methods using rules, statistical models, classifiers, and language models.
Build production anonymization pipelines for processing, training, evaluation, and synthetic-data workflows.
Create frameworks to evaluate privacy risk, data utility, leakage, and adversarial re-identification.
Design systems resilient to new data sources, schema drift, unusual formats, and unexpected sensitive fields.
Collaborate with engineering, research, operations, and customers to translate privacy requirements into safeguards.

At least 2 years of experience building production data or machine learning systems in Python.
Strong Python proficiency and experience building end-to-end data processing pipelines.
Hands-on experience detecting, removing, or anonymizing PII using information extraction, named-entity recognition, classification, or related methods.
Experience benchmarking detection methods and evaluating recall, precision, latency, cost, and downstream data utility.
Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
Experience designing systems resilient to schema drift, unusual data formats, and processing edge cases.
Technical education or equivalent technical background.
Experience with differential privacy, k-anonymity, secure aggregation, or format-preserving encryption is valuable.
Experience with high-throughput or low-latency inference, sensitive data in healthcare, finance, or security, or taking research systems into production is valuable.