Privacy & Anonymization Research Engineer (Remote)

HUD; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive compensation

Top-tier medical/dental/vision

Lunch and dinner in office (onsite)

Holiday break + PTO

401k

Commuter benefits

Unlimited access to tokens for AI APIs

Job summary

HUD is seeking a Research Engineer to build privacy and anonymization systems for sensitive real-world data used in AI training. You will develop methods to detect and remove PII, secrets, and other sensitive information before data enters processing and synthetic data pipelines.

You will own end-to-end privacy protection while preserving data structure and utility, working with engineering, research, operations, and customers. This is a full-time, remote-friendly role with visa-support options.

Qualifications

• 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 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.
• Built data processing pipelines end-to-end without a fully prescribed roadmap.

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.

Skills

Python

Information extraction

Experimental design

Privacy concepts

Data pipelines

Attention to detail

Cross-team collaboration

Tools

Production ML systems

Job description

HUD is seeking a Research Engineer to build privacy and anonymization systems for sensitive real-world data used in AI training. You will develop methods to detect and remove PII, secrets, and other sensitive information before data enters processing and synthetic data pipelines.

You will own end-to-end privacy protection while preserving data structure and utility, working with engineering, research, operations, and customers. This is a full-time, remote-friendly role with visa-support options.