Research Engineer, Privacy and Anonymization

About the role

A fast-growing AI company building infrastructure for AI training data is hiring a Research Engineer, Privacy and Anonymization. The engineer builds systems that make sensitive, real-world data safe while keeping its structure and signal useful for AI training. The role is a privacy and data-protection engineering seat, not a synthetic-task design role, and starts ASAP.

Work is on-site in San Francisco or Singapore, or remote within those time zones, with about 70-80% overlap with the respective business hours. The team is Engineering, and the language is English.

Key responsibilities

• Build systems to detect PII, quasi-identifiers, credentials and other sensitive information, and design transformations based on data type and downstream use case.
• Develop and benchmark detection approaches combining 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.
• Create evaluation frameworks measuring privacy risk and retained data utility, including recall-weighted metrics, leakage tests and adversarial re-identification attempts.
• Design systems robust to new data sources, schema drift, unusual formats and sensitive content in unexpected fields.
• Work with engineering, research, operations and customers to turn privacy requirements into practical technical policies and safeguards.

Required profile

• Strong proficiency in Python and experience building reliable production data or machine-learning systems.
• Experience with information extraction, named-entity recognition, classification or similar methods for detecting rare or sensitive content.
• Ability to compare approaches across recall, precision, latency, cost and downstream data utility.
• Understanding of redaction, masking, pseudonymization, anonymization and synthetic data, and when each is appropriate.
• Attention to detail and ability to reason about leakage paths, edge cases and adversarial failure modes.
• End-to-end data-processing pipeline experience, even without a fully prescribed roadmap.
• Clear written and spoken English and comfort collaborating across teams and time zones.

Required skills

• Python
• Information extraction, named-entity recognition, classification
• Privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation or format-preserving encryption (strong candidates)