Research Engineer, Privacy and Anonymization
H
HUDArtificial Intelligence
Open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones, 70-80% overlap with either San Francisco or Singapore time zones.Full-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonMachine Learning
Requirements
- Strong proficiency in Python
- Experience building reliable production data or ML systems
- Experience with information extraction, named-entity recognition, or classification methods for detecting sensitive content
- Ability to compare approaches across recall, precision, latency, cost, and downstream data utility
- Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data
- Attention to detail regarding leakage paths, edge cases, and adversarial failure modes
- Ability to build data processing pipelines end-to-end
- Experience with differential privacy, k-anonymity, secure aggregation, or format-preserving encryption (nice to have)
- Experience working with sensitive data in healthcare, finance, or security (nice to have)
- Experience building low-latency or high-throughput ML inference systems (nice to have)
- Early-stage startup experience (nice to have)
Responsibilities
- 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
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