Research Engineer, Privacy and Anonymization

Wwshemi; San Francisco (CA); Presencial; USD 130.000 - 225.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Visa sponsorship available

Descripción de la vacante

Wwshemi in San Francisco is seeking a Research Engineer to advance privacy and anonymization for an AI infrastructure team. You will own production methods that protect private information while preserving signal for training and evaluation.

The role focuses on building detection methods, end-to-end data pipelines, and robust safeguards across evolving data sources, with on-site collaboration across teams and customers.

Formación

• 2+ years of experience building production data or ML systems in Python.
• Experience detecting, removing, or anonymizing PII while preserving data characteristics.
• Experience with information extraction or related techniques for sensitive content.
• Experience building data processing pipelines and benchmarking recall/precision/latency.
• Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
• Strong experimental skills handling schema drift and edge cases.

Responsabilidades

• Build privacy/anonymization systems for an AI infra team and own production methods.
• Detect PII, quasi-identifiers, credentials and apply data-type appropriate transformations.
• Develop and benchmark detection methods combining rules, models, and LLMs.
• Create pipelines that anonymize raw data before downstream processing and training.
• Design evaluation frameworks for privacy risk and retained utility.
• Collaborate with engineering, research, operations, and customers to implement safeguards.

Conocimientos

Python

PII detection

Information extraction

End-to-end pipelines

Anonymization

Experimentation

Descripción del empleo

Research Engineer, Privacy and Anonymization

This listing's locations On-site - San Francisco, California, United States

Employment Full-time

Workplace On site

Compensation $130000–225000 per year

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Build privacy and anonymization systems for an AI infrastructure team, helping make sensitive real-world data safe and useful for model training. You will own production methods that protect private information while preserving the structure and signal needed for training and evaluation.

What You'll Do

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, and choose transformations suited to each data type and use case.
• Develop and benchmark detection methods combining rules, statistical models, classifiers, and LLM-based approaches.
• Create production pipelines that anonymize raw data before downstream processing, training, evaluation, or synthetic data generation.
• Build evaluation frameworks for privacy risk and retained 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 in unexpected fields.
• Partner with engineering, research, operations, and customers to turn privacy requirements into practical policies and safeguards.

What We're Looking For

• At least 2 years of experience building production data or ML systems in Python.
• Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.
• Experience with information extraction, named-entity recognition, classification, or related techniques for detecting sensitive or rare content.
• Experience building end-to-end data processing pipelines and benchmarking approaches across recall, precision, latency, cost, and data utility.
• Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data generation.
• Strong experimental skills and experience handling schema drift, unusual formats, and edge cases.
• Technical education or equivalent background; experience with privacy-enhancing technologies or sensitive data in healthcare, finance, or security is valuable.

Base salary: $130,000 to $225,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California, United States.