Research Engineer, Privacy and Anonymization

Pragmatike

. Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information .

Posted 11/12/2026full-timeRemote • SingaporeMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building systems for detecting sensitive information and developing production pipelines for data anonymization. Proficient in Python and experienced in information extraction and privacy-enhancing technologies.

Highest-signal resume keywords

Python ProficiencyProduction Data Systems DevelopmentInformation Extraction TechniquesPrivacy-Enhancing TechnologiesEnd-to-End Data-Processing Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Information ExtractionNamed-Entity RecognitionClassification MethodsData AnonymizationRedaction TechniquesPseudonymizationSynthetic Data GenerationStatistical ModelsDetection ApproachesEvaluation Frameworks

Soft Skills

Attention to DetailClear CommunicationCollaboration Across Teams

Industry Keywords

PII DetectionQuasi-IdentifiersPrivacy Risk MeasurementLeakage TestsAdversarial Re-IdentificationSchema DriftData UtilityLow-Latency ML InferenceHigh-Throughput ML Inference

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information
Design transformations based on data type and downstream use case
Develop and benchmark detection approaches combining rules, statistical models, classifiers, and LLM-based methods
Build production pipelines that anonymize raw data before downstream processing, training, evaluation, or synthetic data generation
Create evaluation frameworks measuring privacy risk and retained data utility
Use 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 detection methods, transformations, evaluation, and the pipeline preceding training and generation

Requirements

What you’ll need

Strong proficiency in Python
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
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 leakage paths, edge cases, and adversarial failure modes
Experience building end-to-end data-processing pipelines without a fully prescribed roadmap
Clear written and spoken English
Comfort collaborating across teams and time zones
Remote candidates must overlap approximately 70–80% with San Francisco (US Pacific) or Singapore business hours
Practical experience with privacy-enhancing technologies, sensitive data, low-latency or high-throughput ML inference, or taking research through production is advantageous

Benefits

Comp & perks

Equal employment opportunities and a fair, inclusive hiring process
Personal data processed solely for recruitment purposes with reasonable safeguards
Opportunity to work on privacy and data-protection systems for AI training
On-site office option in San Francisco or Singapore, or remote work option