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Uber's Engineering Security organization runs technical privacy reviews for product and engineering teams and operates internal privacy tooling that helps engineers handle sensitive data safely. We review thousands of engineering design documents a year and are building AI-powered systems to scale that process. You'll read complex and high-impact architecture documents daily, ask the hard follow-up questions that surface real privacy risks, and write findings engineers can act on. You'll also own the roadmap for our anonymization tooling and partnering with the engineering team that builds it. And as we automate more of our review pipeline with AI, you'll drive the privacy side of technical reviews and improve our systems. This role suits someone with a privacy engineering background who wants broad ownership across reviews, tooling, and AI and who's energized by building the processes and systems that make privacy scale.
Job Responsibility
Perform technical privacy reviews of engineering design documents
Product-manage Uber's anonymization pipelines
Evaluate and improve AI-powered review tooling
Communicate privacy risks and recommendations to leadership, TPMs, and engineering teams
Help establish review templates, standards, and reusable artifacts
Requirements
3+ years working in privacy engineering, privacy red teaming, or as a privacy-focused product manager
Ability to read engineering design documents, understand system architecture and data flows, and ask detailed technical questions
Experience managing a product roadmap for technical tooling
Strong written and verbal English: you produce privacy guidance and risk summaries that both engineering and non-engineering audiences can act on
Understanding Agentic AI privacy risks
Nice to have
CIPT or AIGP certification
Hands-on experience evaluating AI or ML models for privacy use cases
Working knowledge of anonymization techniques (face detection, data masking, pseudonymization)
Experience operating or supporting privacy review programs at scale
Familiarity with GenAI privacy risks and LLM-based tooling
Comfort with lightweight scripting (Python, bash) for evaluation work