Engineering Manager - Privacy Infrastructure

Anthropic•San Francisco, WA

2d•Hybrid

About The Position

Anthropic is seeking an Engineering Manager to build and lead its Privacy Engineering team. This team is responsible for designing and operating the privacy infrastructure that protects user data across AI systems. The role involves owning privacy engineering end-to-end, including privacy-preserving architectures for AI training and inference, data governance and lifecycle systems, and automated controls for regulatory compliance. The manager will lead a team of privacy engineers, scaling the team and its charter as Anthropic grows. The work sits at the intersection of privacy engineering, AI safety, and distributed systems, tackling challenges in protecting user data at scale while maintaining model quality and research velocity.

Requirements

Significant experience managing engineering teams, including hiring and growing teams through periods of ambiguity and rapid change.
Deep expertise in privacy engineering principles: privacy by design, data minimization, and purpose limitation.
Strong technical foundation in data governance and privacy infrastructure (policy enforcement, deletion/retention/lineage systems, encryption key management, audit logging) and the ability to discuss them at a level that earns respect from senior ICs.
Strong understanding of privacy regulations (GDPR, CCPA) and the ability to translate legal requirements into technical solutions.
Experience with data governance, classification, and lifecycle management systems serving large user bases.
Ability to balance technical depth with pragmatic decision-making; you know when to dive deep and when to trust your team.
Strong communication skills: you can translate complex privacy challenges into business terms and vice versa.
Comfort with end-to-end ownership, including defining practices where industry precedent is thin.

Nice To Haves

8+ years of experience managing technical teams.
Experience growing an engineering team and charter through a period of rapid company scaling.
Experience conducting privacy reviews, threat modeling, and risk assessments for production systems.
Proven track record of designing and implementing privacy infrastructure serving millions of users.
Experience at companies during periods of hypergrowth where you've scaled privacy alongside the business.
Exposure to AI/ML infrastructure and the unique privacy demands of large-scale training and inference.

Responsibilities

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.
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.
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.
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.
Lead privacy reviews at scale: Oversee technical privacy reviews and threat modeling for new AI models and features, identifying risks and architecting scalable mitigations.
Enable privacy by default: Champion privacy engineering toolkits and frameworks that let all engineers build privacy-preserving features by default, and embed privacy controls into Claude's inference systems, interfaces, and data pipelines.
Communicate and coordinate: Work closely with security, legal, data infrastructure, research, and go-to-market teams; clearly articulate dependencies, risks, and progress to stakeholders, and advocate for privacy as central to our mission of AI safety.
Stay technically grounded: Maintain enough technical depth to understand your team's work, provide meaningful guidance, and credibly represent privacy concerns in cross-functional discussions.