Remote Privacy & Data Anonymization Engineer

HUD; San Francisco (CA); On-site; SGD 153,000 - 230,000; Full time

Benefits offered by this job

Competitive compensation

100% medical, dental, vision for US-em

Equinox membership

401k

Commuter benefits

Job summary

HUD is seeking a Research Engineer to build privacy and anonymization systems for AI training data. You will own the full pipeline to protect privacy without destroying data utility, applying detection of PII, secrets, and sensitive information before data enters processing and synthesis workflows.

You will work with engineering, research, and customers to translate privacy requirements into practical safeguards, building robust pipelines and evaluation frameworks to measure privacy risk and

Qualifications

• Experience building production data or ML systems in Python.
• Experience with information extraction, NER, classification, or related methods for detecting sensitive content.
• Ability to compare approaches using recall, precision, latency, cost, and data utility.

Responsibilities

• Build systems to detect PII, credentials, and other sensitive data and design transformations.
• Develop and benchmark detection approaches combining rules, statistical models, classifiers, and LLMs.
• Create production pipelines to anonymize data before downstream processing or training.
• Design evaluation frameworks measuring privacy risk and retained data utility.
• Ensure robustness to new data sources, schema drift, and sensitive information in unexpected fields.
• Collaborate with engineering, research, operations, and customers to translate privacy requirements into policies.

Skills

Python

Production ML systems

Information extraction

NER / Classification

Experimentation

Data pipelines

Job description

HUD is seeking a Research Engineer to build privacy and anonymization systems for AI training data. You will own the full pipeline to protect privacy without destroying data utility, applying detection of PII, secrets, and sensitive information before data enters processing and synthesis workflows.

You will work with engineering, research, and customers to translate privacy requirements into practical safeguards, building robust pipelines and evaluation frameworks to measure privacy risk and