Engineering Manager - Privacy Infrastructure

Anthropic Anthropic · AI Frontier · San Francisco, CA · Software Engineering - Infrastructure

Engineering Manager to lead the Privacy Engineering team, responsible for designing and operating privacy infrastructure for user data across AI systems, including training and inference. The role involves building foundational privacy infrastructure, translating regulations into engineering reality, and enabling privacy by default for engineers. This is a management role focused on scaling the team and its charter.

What you'd actually do

  1. Build and lead the team: Recruit, develop, and retain a team of exceptional privacy engineers; establish team charter, practices, and priorities as the team matures
  2. Drive technical strategy: Partner with technical leads, researchers, and legal to set direction for privacy infrastructure across training, inference, and product surfaces: data governance and policy enforcement, deletion and retention at scale, encryption and key management, audit and access transparency, and ML-based PII detection and redaction.
  3. Build foundational privacy infrastructure: Guide the team in building automated data discovery, classification, access controls, audit logging, and lifecycle management systems, plus data governance platforms for tracking lineage, purpose limitation, and retention across distributed AI systems
  4. Translate regulation into engineering: Ensure the team turns complex regulatory requirements (GDPR, CCPA, HIPAA, EU AI Act) into actionable technical implementations and automated compliance controls
  5. Lead privacy reviews at scale: Oversee technical privacy reviews and threat modeling for new AI models and features, identifying risks and architecting scalable mitigations

Skills

Required

  • managing engineering teams
  • hiring and growing teams
  • privacy engineering principles
  • privacy by design
  • data minimization
  • purpose limitation
  • data governance
  • privacy infrastructure
  • policy enforcement
  • deletion/retention/lineage systems
  • encryption key management
  • audit logging
  • privacy regulations (GDPR, CCPA)
  • translating legal requirements into technical solutions
  • data governance
  • classification
  • lifecycle management systems
  • technical depth
  • pragmatic decision-making
  • communication skills
  • translating complex privacy challenges into business terms
  • end-to-end ownership
  • defining practices where industry precedent is thin

Nice to have

  • 8+ years of experience managing technical teams
  • growing an engineering team and charter through rapid company scaling
  • privacy reviews
  • threat modeling
  • risk assessments for production systems
  • designing and implementing privacy infrastructure serving millions of users
  • companies during periods of hypergrowth
  • AI/ML infrastructure
  • unique privacy demands of large-scale training and inference

What the JD emphasized

  • privacy infrastructure that protects user data across our AI systems
  • privacy engineering for Anthropic end-to-end
  • privacy-preserving architectures for AI training and inference
  • foundational data governance and lifecycle systems
  • automated controls that turn complex regulation into engineering reality
  • privacy and compliance posture
  • protecting user data at scale
  • privacy engineering principles
  • privacy by design
  • data minimization
  • purpose limitation
  • privacy engineering principles
  • privacy by design
  • data minimization
  • purpose limitation
  • privacy regulations (GDPR, CCPA)
  • data governance, classification, and lifecycle management systems
  • privacy infrastructure
  • privacy demands of large-scale training and inference

Other signals

  • privacy infrastructure for AI training and inference
  • data governance and lifecycle systems
  • automated controls for regulation
  • ML-based PII detection and redaction
  • privacy by default for engineers