Principal Engineer, Privacy
Anthropic · Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY
About this role
**Principal Engineer, Privacy — Anthropic** ## About the role Anthropic is building frontier AI systems that handle sensitive information at enormous scale. As a **Principal Privacy Engineer**, you’ll set the technical strategy for how personal data is handled across **products, research, and infrastructure**, and lead the hardest, most company-critical privacy work. You’ll work at the intersection of **privacy engineering, distributed systems, and AI safety**—on problems without established answers—leading through influence, quality of thinking, and trust. ## Key responsibilities - Define and drive **technical strategy and architecture** for major privacy areas across product surfaces, research/training data, internal tooling, and multiple clouds. - Identify and lead the **highest-complexity, company-critical** privacy initiatives; guide engineers across many teams and be comfortable building the most difficult pieces yourself. - Architect privacy primitives that make privacy the default and the safe path the fastest one, including: **data discovery, classification, lineage, purpose-based access control, audit, retention, deletion, and export**. - Translate **regulatory, contractual, and consent obligations** (e.g., **GDPR, CCPA, HIPAA, EU AI Act**) into enforced, continuously verified technical controls in partnership with **Legal, Policy, and Compliance**. - Anticipate where AI capabilities, regulation, and customer expectations are heading, and position systems ahead of the curve. - Mentor and develop **staff and senior staff engineers**, raise the bar for privacy design across engineering, and help hire senior privacy talent. - Represent Anthropic’s privacy engineering to **customers, regulators, standards bodies, and the research community**. ## Minimum qualifications - Track record operating as a **Principal engineer (or equivalent)**: setting technical direction adopted by multiple teams and delivering **company-critical, multi-year** initiatives. - Deep experience applying privacy engineering principles in **production systems** with a large user base (e.g., **privacy by design, data minimization, purpose limitation**). - Experience architecting **data governance infrastructure** (classification, lineage, access control, retention, or deletion) across **distributed systems and cloud infrastructure** at scale. - Proficiency in **Python, Go, or similar languages**; effective at writing design docs, making architectural calls, and coding in critical paths. - Understanding of privacy regulations such as **GDPR** and **CCPA**, and ability to work as a peer with legal/policy experts to turn requirements into technical designs. - Strong written and verbal communication that builds alignment across **engineering, research, legal, and product** up to senior leadership. ## Preferred qualifications - Experience building privacy infrastructure/controls for **machine learning or AI systems** (e.g., training data governance, AI agents). - Hands-on experience with privacy-enhancing technologies (e.g., **differential privacy, homomorphic encryption, secure enclaves, secure multi-party computation**). - Experience establishing or re-architecting a privacy engineering practice under **enterprise-customer or regulatory scrutiny**. - Experience briefing executives/auditors/regulators/enterprise customers on privacy risk and defending recommendations under challenge. - History of growing engineers into **Staff-level and beyond**. - Contributio
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