About HUD

HUD's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs.

About the role

We’re looking for a Research Engineer to build the privacy and anonymization systems that make sensitive, real-world data safe and useful for AI training. You’ll develop methods to detect and remove PII, secrets, and other sensitive information from raw data before it enters our processing and synthetic data pipelines. You’ll own the full pipeline for protecting privacy without destroying the structure and signal that make data valuable for training agents.

Responsibilities

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on the 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

• Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards

Experience

You may be a good fit if you have:

• Strong proficiency in Python and experience building reliable production data or ML systems

• Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content

• Strong experimental instincts and the ability to compare approaches across recall, precision, latency, cost, and downstream data utility

• An understanding of the difference between redaction, masking, pseudonymization, anonymization, and synthetic data—and when each is appropriate

• High attention to detail and the ability to reason about subtle leakage paths, edge cases, and adversarial failure modes