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Open nowPosted 4 hours ago

Research Engineer, Privacy and Anonymization

Clera308 open roles

Where
San Francisco
Work mode
On site
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Your applicationOpen nowResearch Engineer, Privacy and AnonymizationClera · San Francisco

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  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. Clera postings stay open a median of 6 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: posted 4 hours ago

Clera median: 6 days open

The posting

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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