Machine Learning Engineer, Anonymization

Mercor•San Francisco, CA

3d•Onsite

About The Position

As a Machine Learning Engineer focused on Anonymization at Mercor, you will be critical in designing and implementing our industry-best data privacy pipeline. You'll operate at the intersection of advanced ML techniques, sensitive data handling, and robust backend engineering. Your primary focus will be shipping production systems that employ state-of-the-art anonymization and de-identification methods, ensuring maximum utility of our vast data network while maintaining the highest standards of data integrity and regulatory compliance. This role requires bringing deep statistical and modeling rigor to challenging problems in privacy-preserving data access.

Requirements

Strong backend engineering skills (ex. Python/Django or similar) plus a solid foundation in applied ML and statistics.
Proven experience shipping production systems or ML-driven products end-to-end.
High ownership and comfort operating in ambiguous, fast-changing environments.
Demonstrated knowledge of industry best practices and common frameworks for data privacy and security.

Responsibilities

Design, implement, and productionize advanced ML models and techniques (like federated learning, differential privacy, or synthetic data generation) for data anonymization.
Build and maintain the core backend infrastructure and APIs to securely process and serve anonymized data at Mercor's scale.
Benchmark our anonymization pipeline against industry best practices and regulatory standards (e.g., k-anonymity), continuously running experiments to improve both privacy guarantees and data utility.
Collaborate cross-functionally with Legal, Security, and Engineering teams to translate compliance requirements into robust, model-driven solutions.
Act as the subject matter expert on data anonymization, flexing between applied ML, complex data pipeline engineering, and driving architectural decisions for data privacy.