Research Engineer, Privacy and Anonymization (Pacific Time)

Pragmatike

3d

About The Position

The company is building AI training-data infrastructure through a marketplace that connects data creators with AI labs. It is seeking a Research Engineer, Privacy and Anonymization to develop systems that make sensitive real-world data safe and useful for AI training. The role focuses on detecting and removing personally identifiable information, secrets, and other sensitive content from raw data while preserving the structure and signal needed for downstream training and agent tasks.

This is a privacy and data-protection engineering role, rather than a synthetic-task design role. The engineer will own the pipeline that operates before downstream processing, training, evaluation, and synthetic data generation. The position is full-time and starts ASAP. It can be performed on-site in San Francisco or Singapore, or remotely from Singapore or US Pacific time zones. Remote working hours should overlap approximately 70–80% with San Francisco or Singapore business hours; the exact schedule will be agreed during hiring.

Requirements

• 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.
• Strong experimental instincts and the ability to compare approaches across recall, precision, latency, cost, and downstream data utility.
• Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data, including 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, including in situations without a fully prescribed roadmap.
• Clear written and spoken English, and comfort collaborating across teams and time zones.

Nice To Haves

• Hands-on experience with privacy-enhancing technologies such as differential privacy, k-anonymity, secure aggregation, or format-preserving encryption.
• Experience working with sensitive data in healthcare, finance, or security.
• Experience building low-latency or high-throughput ML inference or data-processing systems.
• Experience taking work from early research through production.

Responsibilities

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, and design transformations appropriate to the 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 for privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
• Design robust systems for 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed