Research Engineer, Privacy and Anonymization
Role Overview
Build privacy and anonymization systems for an AI infrastructure team, helping make sensitive real-world data safer to use in model training and evaluation. You will develop end-to-end methods that protect sensitive information while preserving the structure and utility of data.
Key Skills for This Role
Full Job Posting
About the Role
Build privacy and anonymization systems for an AI infrastructure team, helping make sensitive real-world data safer to use in model training and evaluation. You will develop end-to-end methods that protect sensitive information while preserving the structure and utility of data.
What You'll Do
Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, then choose transformations suited to the data and its downstream use.
Develop and benchmark detection approaches using rules, statistical models, classifiers, and LLM-based methods.
Build production pipelines that anonymize data before it enters processing, training, evaluation, or synthetic data workflows.
Create evaluation frameworks for privacy risk and retained utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
Design systems that handle new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
Collaborate across engineering, research, operations, and customer-facing teams to turn privacy requirements into practical safeguards.
What We're Looking For
At least 2 years of experience building production data or ML systems with strong Python skills.
Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.
Experience with information extraction, named-entity recognition, classification, or related methods for detecting sensitive or rare content.
Experience building end-to-end data processing pipelines and working through open-ended problems from research to production.
Ability to compare methods across recall, precision, latency, cost, and downstream 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 is valuable.
Experience with sensitive data in healthcare, finance, security, or related fields, and with high-throughput or low-latency ML systems, is a plus.
Compensation & Benefits
Base salary: $130,000 to $225,000 USD annually. Visa sponsorship is available.
Location
- On-site in San Francisco, California, United States.
Qualifications
- At least 2 years of experience building production data or ML systems with strong Python skills.
- Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.
- Experience with information extraction, named-entity recognition, classification, or related methods for detecting sensitive or rare content.
- Experience building end-to-end data processing pipelines and working through open-ended problems from research to production.
- Ability to compare methods across recall, precision, latency, cost, and downstream 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 is valuable.
- Compensation & Benefits
- Base salary: $130,000 to $225,000 USD annually. Visa sponsorship is available.
- Location
- On-site in San Francisco, California, United States.
Preferred Qualifications
- Experience with sensitive data in healthcare, finance, security, or related fields, and with high-throughput or low-latency ML systems, is a plus.
Responsibilities
- Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information, then choose transformations suited to the data and its downstream use.
- Develop and benchmark detection approaches using rules, statistical models, classifiers, and LLM-based methods.
- Build production pipelines that anonymize data before it enters processing, training, evaluation, or synthetic data workflows.
- Create evaluation frameworks for privacy risk and retained utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
- Design systems that handle new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
- Collaborate across engineering, research, operations, and customer-facing teams to turn privacy requirements into practical safeguards.
- At least 2 years of experience building production data or ML systems with strong Python skills.
- Hands-on experience detecting, removing, or anonymizing PII while preserving useful data characteristics.
- Experience with information extraction, named-entity recognition, classification, or related methods for detecting sensitive or rare content.
- Experience building end-to-end data processing pipelines and working through open-ended problems from research to production.
- Ability to compare methods across recall, precision, latency, cost, and downstream 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 is valuable.
- Experience with sensitive data in healthcare, finance, security, or related fields, and with high-throughput or low-latency ML systems, is a plus.
- Base salary: $130,000 to $225,000 USD annually. Visa sponsorship is available.
- On-site in San Francisco, California, United States.
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