Research Engineer Privacy and Anonymization

Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones) Start Date: ASAP Languages: English Team: Engineering About the Role Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand. We are looking for a Research Engineer, Privacy and Anonymization to build the systems that make sensitive, real-world data safe and still useful for AI training. You will develop methods to detect and remove personally identifiable information, secrets and other sensitive content from raw data before it enters processing and synthetic data pipelines — protecting privacy without destroying the structure and signal that make the data valuable for training agents. This is a privacy and data-protection engineering seat, not a synthetic-task design role. You will own the pipeline that stands in front of downstream processing, training, evaluation and synthetic generation. This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring. 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 We're Looking For 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