Research Engineer, Privacy and Anonymization

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

Job summary

Wwshemi in San Francisco is seeking a data/privacy engineer to help build privacy and anonymization systems for AI training data. You will design and implement methods that protect personal information while preserving data utility.

You will work on production pipelines to detect PII, apply appropriate transformations, and benchmark privacy and utility metrics across real-world data scenarios.

Qualifications

• At least 2 years of experience building production data or ML systems in Python.
• Experience with PII detection, removal, or anonymization, and with information extraction, named‑entity recognition, classification, or related techniques.
• 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.
• Experience with privacy‑enhancing technologies such as differential privacy, k‑anonymity, secure aggregation, or format‑preserving encryption.
• Strong experimental judgment and attention to edge cases; experience with sensitive data or high‑throughput ML and data systems is valuable.

Responsibilities

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, then select 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 data before downstream processing, training, evaluation, or synthetic data generation.
• Build evaluation frameworks for privacy risk and retained data utility, including leakage tests and adversarial re‑identification attempts.
• Design robust systems that handle new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
• Work with engineering, research, operations, and customers to turn privacy requirements into practical safeguards.

Skills

Python

PII detection

Data pipelines

Anonymization

Privacy tech

Experimentation

Job description

About the Role

Join a team building privacy and anonymization systems for sensitive real-world data used in AI training. You will develop methods that protect personal and confidential information while preserving the structure and signal that make data useful.

What You'll Do

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, then select 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 data before downstream processing, training, evaluation, or synthetic data generation.
• Build evaluation frameworks for privacy risk and retained data utility, including leakage tests and adversarial re-identification attempts.
• Design robust systems that handle new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
• Work with engineering, research, operations, and customers to turn privacy requirements into practical safeguards.

What We're Looking For

• At least 2 years of experience building production data or ML systems in Python, with hands-on experience transforming PII while preserving useful data characteristics.
• Experience with PII detection, removal, or anonymization, and with information extraction, named‑entity recognition, classification, or related techniques.
• 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.
• Experience with privacy‑enhancing technologies such as differential privacy, k‑anonymity, secure aggregation, or format‑preserving encryption.
• Strong experimental judgment and attention to edge cases; experience with sensitive data or high‑throughput ML and data systems is valuable.

Compensation & Benefits

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

Location

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