Privacy & Anonymization Research Engineer — On-site SF

Wwshemi; San Francisco (CA); On-site; USD 130,000 - 225,000; Full time

Benefits offered by this job

Visa sponsorship available

Job summary

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.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

PII detection

Information extraction

End-to-end pipelines

Anonymization

Experimentation

Job description

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.