Research Engineer, Privacy and Anonymization

Pragmatike

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

Posted 11/12/2026full-timeRemote • California • United States, Singapore, Colombia, Canada, Costa Rica, MexicoMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong proficiency in Python and experience in building reliable production data or machine-learning systems, with a focus on information extraction and privacy-preserving techniques. Capable of designing robust data-processing pipelines and collaborating effectively across teams to translate privacy requirements into actionable technical policies.

Highest-signal resume keywords

Python ProficiencyProduction Data Systems DevelopmentInformation Extraction TechniquesData-Processing Pipeline DesignPrivacy-Preserving Techniques

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Information ExtractionNamed-Entity RecognitionClassification MethodsData AnonymizationData RedactionPseudonymizationSynthetic Data GenerationStatistical ModelsMachine Learning SystemsAdversarial Re-Identification

Soft Skills

Attention to DetailClear CommunicationCollaboration Across TeamsTechnical AptitudeProblem-Solving

Industry Keywords

Healthcare DataFinance DataSecurity DataDifferential PrivacyK-AnonymitySecure AggregationFormat-Preserving Encryption

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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

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 subtle 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
Technical aptitude and learning potential
Candidates may have experience with differential privacy, k-anonymity, secure aggregation or format-preserving encryption
Candidates may have worked with sensitive data in healthcare, finance or security
Candidates may have built low-latency or high-throughput ML inference or data-processing systems
Candidates may have taken work from early research through production
Ability to overlap approximately 70–80% with San Francisco US Pacific or Singapore business hours

Benefits

Comp & perks

Equal employment opportunity and non-discrimination commitment
Fair and inclusive hiring process
Personal data processed solely for recruitment purposes with reasonable safeguards
Full-time role
Remote work option for Singapore or US Pacific time zones
On-site work option in San Francisco or Singapore
Opportunity to build privacy systems enabling sensitive data use for AI training
Opportunity to work on privacy, anonymization, data utility, schema drift and adversarial failure modes
Collaboration with engineering, research, operations, customers and international teams