Research Engineer, Privacy and Anonymization (Singapore Time)

Overview

Research Engineer, Privacy and Anonymization builds the systems that detect and remove personally identifiable information, secrets and other sensitive content from raw data before it enters processing and synthetic data pipelines, protecting privacy while keeping the data useful for AI training.

What you'll do

• Build systems to detect PII, quasi-identifiers, credentials and other sensitive information, and design transformations based on the data type and the 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.
• 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 stay 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.

What you'll need

• Strong proficiency in Python, and experience building reliable production data or machine-learning 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 the difference between 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.
• Experience building data-processing pipelines end to end, including where there was no fully prescribed roadmap.
• Clear written and spoken English, and comfort collaborating across teams and time zones.

Nice to have

• Hands-on experience with privacy-enhancing technologies (such as differential privacy, k-anonymity, secure aggregation or format-preserving encryption).
• Experience working with sensitive data in healthcare, finance or security.
• Experience building low-latency or high-throughput ML inference or data-processing systems.
• Experience taking work from early research through to production.

Details

• Full-time role, starting ASAP.
• On-site in San Francisco or Singapore, or remote (Singapore or US Pacific time zones).
• Remote candidates need working hours overlapping about 70–80% with San Francisco (US Pacific) or Singapore business hours; exact daily schedule agreed during hiring.
• Languages: English.