# Research Engineer, Privacy and Anonymization at Clera

Research Engineer, Privacy and Anonymization at Clera in San Francisco. On-site, full-time, paying $130k–$225k. Posted November 5, 2026.

- Company: Clera
- Location: San Francisco
- Workplace: On-site
- Job type: Full-time
- Pay: $130k–$225k
- Experience: 2+ years
- Posted: 2026-11-05
- Apply: https://jobs.ashbyhq.com/clera/904a9907-9aac-490d-8fd7-e474fb612cfe/application
- Source: https://jobmagic.ai/jobs/research-engineer-privacy-and-anonymization-clera-cvpmvt

## Requirements

- 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.

## Description

### About the Role

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.

### Compensation & Benefits

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

### Location

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