Privacy Program Manager

Anthropic•San Francisco, CA

23h•Hybrid

About The Position

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic's Privacy Program team sits at the intersection of Legal, Engineering, Product, and Research — translating complex privacy requirements into practical, scalable implementation across our products and internal systems. As a Privacy Program Manager, you will own the operational backbone of Anthropic's privacy program, ensuring that privacy controls are not just documented but auditable, verifiable, and continuously improving. This role is equal parts program management and hands-on privacy work. You'll partner closely with Product and Privacy Counsel, Privacy Engineering, and product teams to embed privacy-by-design into how we build and ship AI products — from consumer features to enterprise deployments. You'll bring technical depth and privacy expertise to understand data flows, system architectures, and user data handling workflows, combined with the program management rigor to drive cross-functional initiatives to completion.

Requirements

7+ years of experience in privacy program management, privacy engineering, or a related role at a technology company, with demonstrated ownership of privacy operations at scale
Strong understanding of global privacy regulations (GDPR, CCPA/CPRA) and sector-specific frameworks (HIPAA, FINRA, FERPA, FedRAMP) with practical experience applying them to product development
Technical fluency — you can read a data flow diagram, understand system architectures, ask incisive questions about data handling, and translate between legal requirements and engineering implementation without needing to write code yourself
Proven track record of driving cross-functional programs across legal, product, engineering, and security teams in fast-moving environments
Experience building or significantly improving privacy review processes, privacy impact assessments, or privacy control frameworks
Comfortable operating in ambiguity — you can define structure where none exists and prioritize ruthlessly when everything feels urgent
Excellent written and verbal communication skills, with the ability to produce clear, actionable documentation for both technical and non-technical audiences

Nice To Haves

Experience at a company navigating hypergrowth, where privacy programs had to scale alongside rapid product development
Familiarity with privacy-enhancing technologies and their practical application in product contexts
Experience with enterprise compliance programs and customer-facing privacy controls (e.g., data retention policies, access controls, audit logging)
Background in AI, machine learning, or adjacent technology domains

Responsibilities

Design, implement, and maintain auditable privacy controls across Anthropic's products and internal systems, including automated control monitoring and remediation workflows
Partner with Product and Privacy Counsel to run and continuously improve privacy review processes for new product launches, features, and integrations
Drive enterprise privacy readiness across key verticals (e.g., HIPAA, financial services, education), translating regulatory requirements into concrete product and engineering work
Collaborate with Privacy Engineering to scope and prioritize privacy-enhancing improvements to product features, data pipelines, and user data handling workflows
Create and maintain internal privacy guidance, best practices documentation, and training materials that enable cross-functional teams to build with privacy in mind
Manage privacy program operations including tracking privacy debt, coordinating remediation efforts, and reporting on program health metrics
Serve as a key bridge between legal and technical teams — translating privacy requirements into actionable engineering specifications and ensuring implementation fidelity