# Engineering Manager - Privacy Infrastructure **Company**: Anthropic **Location**: San Francisco, CA | Seattle, WA **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Salary**: $405,000-$485,000 USD **Category**: Engineering **Industry**: Technology **Wikidata**: https://www.wikidata.org/wiki/Q116758847 **Apply**: https://job-boards.greenhouse.io/anthropic/jobs/5227810008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_7431066f-4e7 ## Description We're looking for an Engineering Manager to build and lead our Privacy Engineering team. This role involves designing and operating the privacy infrastructure that protects user data across our AI systems. You'll have an outsized impact in shaping how Anthropic builds world-class privacy into Claude from the ground up. This is a role with extraordinary scope and leverage. You'll own privacy engineering for Anthropic end-to-end. The work spans privacy-preserving architectures for AI training and inference, foundational data governance and lifecycle systems, and the automated controls that turn complex regulation into engineering reality. You'll lead a team of talented privacy engineers that builds and operates the platform and infra frameworks underpinning Anthropic's privacy and compliance posture. Your job is to scale the team and its charter as Anthropic grows. Working at the intersection of privacy engineering, AI safety, and distributed systems, your team will solve novel challenges in protecting user data at scale, handling billions of conversations while maintaining model quality and research velocity. If owning the whole problem and having an outsized impact on how a frontier AI lab protects its users sounds compelling, this role might be for you. **Key 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 - 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 **About You:** We're looking for a technical leader who thinks of themselves as a problem-solver and team-builder first. The ideal candidate has: - 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. - 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 **Preferred:** - 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 **Logistics:** - Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience - Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience - Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position - Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. - Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. ## Skills ### Required - Privacy engineering - Data governance - Lifecycle management - Encryption key management - Audit logging - ML-based PII detection and redaction - Data governance platforms - Tracking lineage - Purpose limitation - Retention across distributed AI systems ### Nice to have - AI/ML infrastructure - Large-scale training and inference - Hypergrowth company scaling - Conducting privacy reviews - Threat modeling - Risk assessments for production systems --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/anthropic/jobs/5227810008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)