Research Engineer, Privacy and Anonymization (Visa Sponsorship - San Francisco, United States)

Visa sponsorship available. A research and engineering team at a company building privacy and anonymization systems for sensitive data used in AI training. You will help protect personal and confidential information while keeping the structure and usefulness of the data intact for downstream work. The role combines applied research with production engineering in a fast-moving, on-site environment.

What you'll do

• Build systems that detect personally identifiable information, quasi-identifiers, credentials and other sensitive content, then choose transformations suited to the data and its intended use.
• Develop and benchmark detection methods that mix rules, statistical models, classifiers and language models.
• Build production pipelines that anonymize raw data before it reaches processing, training, evaluation or synthetic data workflows.
• Create evaluation frameworks for privacy risk and retained data utility, including recall-weighted metrics, leakage tests and adversarial re-identification attempts.
• Design systems that stay robust to new data sources, schema drift, unusual formats and sensitive information in unexpected fields.
• Work with engineering, research, operations and customers to turn privacy requirements into practical policies and safeguards.

What you need

• At least 2 years of experience building production data or machine learning systems, with strong Python skills.
• A technical education or equivalent technical background, and experience building end-to-end data processing pipelines.
• Hands-on experience detecting, removing or anonymizing PII while preserving useful characteristics, using information extraction, named-entity recognition, classification or related methods.
• Experience benchmarking detection methods and measuring recall, precision, latency, cost and downstream data utility.
• Understanding of redaction, masking, pseudonymization, anonymization and synthetic data generation.
• Experience designing systems resilient to schema drift and processing edge cases. Experience with privacy-enhancing technologies (differential privacy, k-anonymity, secure aggregation, format-preserving encryption), high-throughput inference, or work with sensitive data in healthcare, finance or security is a plus.

How to apply

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