Research Engineer, Privacy and Anonymization (Pacific Time)

Pragmatike; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Pragmatike is seeking a privacy-focused engineer to build anonymization systems that safely enable AI training while preserving data utility. You will own the processing layer that detects and transforms sensitive content before data reaches training, evaluation, or synthetic-data pipelines.

You will develop methods to measure privacy risk and data utility, and work with engineering, research, and customers to implement practical safeguards and policies across time zones.

Qualifications

• Strong Python skills and experience building dependable production data or ML systems.
• Experience with information extraction, named-entity recognition, classification, or related approaches to finding rare or sensitive content.
• Ability to compare methods across recall, precision, latency, cost, and downstream utility.
• Understanding of when redaction, masking, pseudonymization, anonymization, and synthetic data are appropriate.
• Careful reasoning about edge cases, subtle leakage paths, and adversarial failure modes.
• Experience building end-to-end data pipelines, including work without a fully specified roadmap, and clear English communication across teams and time zones.

Responsibilities

• Detect personally identifying details, quasi-identifiers, credentials, and other sensitive information, then select transformations suited to the data and its intended use.
• Develop and compare detection methods using rules, statistical techniques, classifiers, and language models.
• Build production pipelines that transform raw data before downstream processing, training, evaluation, or generation.
• Create evaluation methods for privacy risk and data usefulness, including leakage checks and adversarial re-identification testing.
• Make systems robust to new sources, changing schemas, unusual formats, and sensitive information in unexpected fields.
• Work with engineering, research, operations, and customers to turn privacy needs into practical safeguards and policies.

Skills

Python

NER

End-to-end pipelines

English communication

Job description

Role overview

Build privacy and anonymization systems that make sensitive real-world data safer to use in AI training while preserving the structure and information that downstream work needs. Own the processing layer that detects and transforms sensitive content before data reaches training, evaluation, or synthetic-data pipelines, and develop ways to measure both privacy risk and retained utility.

Responsibilities

• Detect personally identifying details, quasi-identifiers, credentials, and other sensitive information, then select transformations suited to the data and its intended use.


• Develop and compare detection methods using rules, statistical techniques, classifiers, and language models.


• Build production pipelines that transform raw data before downstream processing, training, evaluation, or generation.


• Create evaluation methods for privacy risk and data usefulness, including leakage checks and adversarial re-identification testing.


• Make systems robust to new sources, changing schemas, unusual formats, and sensitive information in unexpected fields.


• Work with engineering, research, operations, and customers to turn privacy needs into practical safeguards and policies.

Requirements

• Strong Python skills and experience building dependable production data or machine-learning systems.


• Experience with information extraction, named-entity recognition, classification, or related approaches to finding rare or sensitive content.


• Ability to compare methods across recall, precision, latency, cost, and downstream utility.


• Understanding of when redaction, masking, pseudonymization, anonymization, and synthetic data are appropriate.


• Careful reasoning about edge cases, subtle leakage paths, and adversarial failure modes.


• Experience building end-to-end data pipelines, including work without a fully specified roadmap, and clear English communication across teams and time zones.

Nice to have

• Experience with privacy-enhancing methods such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption.


• Work with sensitive data in healthcare, finance, or security; high-throughput or low-latency data and ML systems; or research carried through to production.

Benefits and work setup

Full-time role with offices in San Francisco and Singapore, or remote work from Singapore or U.S. Pacific time zones. Remote schedules should overlap approximately 70-80% with business hours in either location; the daily schedule is agreed during hiring. The start date is listed as ASAP.