Privacy Technologist II - AI

Uber Uber · Consumer · Sao Paulo, Brazil · Engineering

This role involves performing technical privacy reviews of engineering design documents, managing anonymization pipelines, and evaluating/improving AI-powered review tooling. The focus is on driving the privacy aspects of AI automation and ensuring safe handling of sensitive data within AI systems, particularly agentic AI and GenAI.

What you'd actually do

  1. Perform technical privacy reviews of engineering design documents. Dig into architecture details, ask targeted follow-up questions, identify privacy risks, and write clear recommendations that engineering teams act on.
  2. Product-manage Uber's anonymization pipelines. Set roadmap priorities, evaluate model options, and partner with the engineering team that builds the tooling.
  3. Evaluate and improve AI-powered review tooling. Review AI-generated privacy findings, calculate and report metrics, identify gaps the models miss, and contribute to the knowledge bases and feedback loops that make the system more accurate over time.
  4. Communicate privacy risks and recommendations to leadership, TPMs, and engineering teams in formats those audiences can act on.
  5. Help establish review templates, standards, and reusable artifacts so privacy reviews and tooling decisions scale beyond your direct involvement.

Skills

Required

  • privacy engineering
  • privacy red teaming
  • privacy-focused product manager
  • understand system architecture and data flows
  • ask detailed technical questions
  • managing a product roadmap for technical tooling
  • written and verbal English
  • Understanding Agentic AI privacy risks

Nice to have

  • CIPT or AIGP certification
  • evaluating AI or ML models for privacy use cases
  • anonymization techniques (face detection, data masking, pseudonymization)
  • operating or supporting privacy review programs at scale
  • Familiarity with GenAI privacy risks and LLM-based tooling
  • lightweight scripting (Python, bash) for evaluation work

What the JD emphasized

  • AI-powered systems
  • automate more of our review pipeline with AI
  • AI-powered review tooling
  • Agentic AI privacy risks
  • GenAI privacy risks and LLM-based tooling

Other signals

  • AI-powered systems to scale that process
  • automate more of our review pipeline with AI
  • Evaluate and improve AI-powered review tooling
  • Review AI-generated privacy findings
  • Understanding Agentic AI privacy risks
  • Familiarity with GenAI privacy risks and LLM-based tooling