Research Engineer, Privacy and Anonymization (Mid-Level)

22 hours ago

H1B sponsor

Base Salary

$130k - $225k/yr

Responsibilities

Build systems that detect PII, quasi-identifiers, credentials, and other sensitive information and transform it according to the data type and use case.
Develop and benchmark rules, statistical models, classifiers, and LLM-based detection methods.
Create production anonymization pipelines for downstream processing, training, evaluation, and synthetic data generation.
Measure privacy risk and retained data utility using evaluation frameworks, leakage tests, and adversarial re-identification attempts.
Design robust systems for new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
Partner with engineering, research, operations, and customers to implement practical privacy safeguards.

At least 2 years of experience building production data or ML systems in Python.
Hands-on experience detecting, removing, or transforming 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 benchmarking recall, precision, latency, cost, and data utility.
Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
Experience handling schema drift, unusual formats, and edge cases.
Familiarity 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 low-latency or high-throughput ML systems is beneficial.