Research Engineer, Privacy and Anonymization

Responsibilities: — Build systems to detect personally identifiable information (PII), quasi-identifiers, credentials, and other sensitive data, designing transformations tailored to data types and downstream use cases. — Develop and benchmark detection methods combining rules, statistical models, classifiers, and large language model (LLM)-based approaches. — Construct production pipelines to anonymize raw data before downstream processing, training, evaluation, or synthetic data generation. — Create evaluation frameworks to assess privacy risk and data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts. — Design robust systems resilient to new data sources, schema drift, unusual formats, and hidden sensitive information. — Collaborate with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards. Requirements: — 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 with the 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. — High attention to detail and ability to reason about subtle leakage paths, edge cases, and adversarial failure modes. — Experience building end-to-end data-processing pipelines without fully prescribed roadmaps. — Clear written and spoken English and comfort collaborating across teams and time zones. — Preferred but not required: hands-on experience with privacy-enhancing technologies (differential privacy, k-anonymity, secure aggregation, format-preserving encryption), work with sensitive data in healthcare, finance or security, building low-latency or high-throughput ML inference or data-processing systems, or taking work from early research through to production. — Technical aptitude and learning potential are valued more than years of experience. Benefits: — Full-time role starting ASAP. — Work on-site in San Francisco or Singapore, or remotely with working hours overlapping about 70–80% with either US Pacific or Singapore business hours. — Exact daily schedule to be agreed during hiring. — Equal opportunity employer committed to fair and inclusive hiring without discrimination. Candidates motivated to apply even if not meeting every requirement are encouraged to do so.