Senior Staff Privacy Engineer

Quick Facts

• Senior Staff individual contributor privacy engineering role focused on AI/ML privacy protections.

Description

AI products rely on sensitive data, so privacy is built into the workflow end-to-end—from what is collected and how it’s used to the controls applied across models, tools, logs, and data pipelines. You will write production code and shape architecture and protections, improving sensitive data detection/masking, assessing privacy risks in ML models, and owning implementation of customer data-use and retention controls. You will collaborate with Privacy, ML, Security, Product, and Infrastructure teams from design through production.

Responsibilities

• Assess, benchmark, and select ML models and techniques to meet privacy requirements; build representative evaluation datasets and compare coverage, false positives, latency, cost, and product quality impact.
• Own implementation of privacy models across prototype to production, including training/fine-tuning as needed, building services/APIs, and optimizing inference performance and reliability.
• Build and deploy sensitive data detection, masking, redaction, and tokenization across logs, traces, APIs, analytics, and annotation tools; enforce consistent masking controls and access to unmasked content.
• Conduct privacy evaluations for ML systems (e.g., memorization, training-data extraction, membership inference, sensitive disclosure via retrieval/tool use) and implement mitigations with regression tests in release workflows.
• Write and maintain code that enforces customer requirements for data use, access, retention, and deletion across services and data stores, distinguishing permitted inference, evaluation, annotation, and training.
• Prototype privacy-preserving evaluation approaches and synthetic data; measure privacy risk vs. utility, validate assumptions, and integrate into ML pipelines.
• Lead or partner on privacy design reviews; identify gaps in data flows/architectures and implement code changes; build reusable components and standards.
• Monitor privacy controls and models in production, investigate failures, ship improvements, and use production feedback to address regressions and customer-reported privacy issues.

Requirements

• 7+ years building production software, with substantial privacy engineering and applied ML experience.
• Strong Python skills; experience with Go, Java, C++, or another production systems language; shipped, tested, and operated production software.
• Hands-on experience evaluating/selecting/deploying ML models, including dataset curation, model adaptation or fine-tuning, error analysis, and model serving.
• Experience building sensitive data detection and masking systems, including accuracy/performance/integration tradeoffs.
• Experience assessing privacy risks in ML and LLM systems and implementing mitigations with ability to explain test evidence and remaining uncertainty.
• Deep understanding of data minimization, pseudonymization, anonymization, and re-identification risk (and limits of removing direct identifiers).
• Deep experience building data protection and lifecycle controls in distributed cloud systems.
• Demonstrated ability to take ambiguous problems from design through delivery, set technical direction across teams, and mentor engineers.

Benefits

• Work with cross-functional teams to deliver privacy-by-design protections in production.
• Hands-on role writing production code while setting technical direction across teams.
• Continuous improvement through monitoring, error analysis, and release regression testing.