Research Engineer, Privacy and Anonymization
At a glance
Mid-level AI Research role at Clera. San Francisco · full-time · $130,000–$225,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build detectors for personal identifiers, quasi-identifiers, credentials and other sensitive fields, then choose suitable transformations
- Combine rules, statistical models, classifiers and language-model methods to detect sensitive data and benchmark their results
- Run anonymization pipelines before data enters processing, training, evaluation or synthetic-data workflows
- Measure privacy risk and remaining data usefulness through leakage checks and attempts to re-identify records
- Design pipelines that cope with changing schemas, unfamiliar formats, new sources and sensitive data in unexpected fields
What you bring
- At least two years building production data or machine-learning systems in Python, including PII transformations that preserve useful characteristics
- Hands-on background in detecting, removing or anonymizing PII, with extraction, entity recognition, classification or related methods
- Experience building complete data pipelines and comparing approaches on precision, recall, speed, cost and data utility
- Working knowledge of privacy techniques such as differential privacy, k-anonymity or another listed method
Who this fits
This mid-level, individual-contributor role suits someone who has built production data or ML systems and can balance privacy protection with data utility. The job is on-site in San Francisco, and visa sponsorship is available.
From the employer
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.
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