Research Engineer, Privacy and Anonymization

1 day ago

H1B sponsor

Base Salary

$130k - $225k/yr

Responsibilities

Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and select appropriate transformations.
Develop and benchmark rule-based, statistical, classifier, and LLM-based detection approaches.
Build production anonymization pipelines for processing, training, evaluation, and synthetic data workflows.
Create privacy-risk and retained-utility evaluation frameworks using recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
Design systems that handle new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
Collaborate with engineering, research, operations, and customer-facing teams to translate privacy requirements into safeguards.

At least 2 years of experience building production data or ML systems, with strong Python skills.
Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.
Experience with information extraction, named-entity recognition, classification, or related sensitive-content detection methods.
Experience building end-to-end data processing pipelines and taking open-ended problems from research to production.
Ability to compare methods across recall, precision, latency, cost, and downstream data utility.
Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
Experience with differential privacy, k-anonymity, secure aggregation, or format-preserving encryption is valuable.
Experience with sensitive data in healthcare, finance, security, or related fields, and with high-throughput or low-latency ML systems, is a plus.