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 detect and remove personally identifiable information, secrets and other sensitive content from raw data before it enters processing, training, evaluation and synthetic data pipelines, protecting privacy while keeping the data's structure and signal. The role is full-time, starting ASAP, and is available on-site in San Francisco or Singapore, or remotely within those time zones.

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 reaches 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 information 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.
• Experience building end-to-end data-processing pipelines, including where there was no 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)