Research Engineer, Privacy and Anonymization

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on the data type and 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 workflows
• 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 remain 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
• You may be a good fit if you have:
• Strong proficiency in Python and experience building reliable production data or ML 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
• Built data processing pipelines end-to-end without a fully prescribed roadmap
• Hands-on experience with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, format-preserving encryption, etc.
• Worked with sensitive data in areas such as healthcare, finance, or security
• Built low-latency or high-throughput ML inference and data-processing systems
• Worked in unstructured problem spaces and take ownership from early research through production deployment
• Early-stage startup experience and strong communication skills for collaboration across teams and time zones
• We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.
• Team Size: ~25 people currently, mostly full-time in-person, but some remote.
• Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), the former #1 ranked Catan player in the world, serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.
• Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand.
• Employment: Full-time.
• Location: We have offices in San Francisco or Singapore but are open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones.

None flagged.