Research Engineer

• Build systems to detect PII, quasi-identifiers, credentials and other sensitive information
• Design data transformations based on data type and downstream use case
• Develop and benchmark detection approaches using rules, statistical models, classifiers and LLM-based methods
• Build production anonymization pipelines before downstream processing, training, evaluation or synthetic data generation
• Create evaluation frameworks measuring privacy risk and retained data utility
• Implement recall-weighted metrics, leakage tests and adversarial re-identification attempts
• Design systems robust to new data sources, schema drift, unusual formats and sensitive information in unexpected fields
• Work with engineering, research, operations and customers to translate privacy requirements into technical policies and safeguards
• Own the pipeline in front of downstream processing, training, evaluation and synthetic generation

Explore Research Engineer, Privacy and Anonymization at Pragmatike. Review requirements and application details.