Principal Engineer, Privacy

Key responsibilities

• Define and drive technical strategy and architecture for major areas of privacy, spanning product surfaces, research and training data, internal tooling, and multiple clouds
• Identify and lead the highest-complexity, company-critical privacy initiatives, guiding engineers across many teams. Be comfortable building the most difficult pieces yourself
• Architect the primitives that make privacy the default and the safe path the fastest one: data discovery, classification, lineage, purpose-based access control, audit, retention, deletion, and export
• Turn regulatory, contractual, and consent obligations (e.g., GDPR, CCPA, HIPAA, the EU AI Act) into enforced, continuously verified technical controls in partnership with Legal, Policy, and Compliance team
• Anticipate where AI capabilities, regulation, and customer expectations are heading, and position our systems ahead of them
• 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

• A track record of operating as a Principal engineer or equivalent: setting technical direction that multiple teams adopted, and delivering company-critical, multi-year initiatives
• Deep experience applying privacy engineering principles in production systems with a large user base, including privacy by design, data minimization, and 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; you are effective writing design docs, making architectural calls, and coding in critical paths
• Understanding of privacy regulations such as GDPR and CCPA, and the ability to work as a peer with legal and policy experts to turn requirements into technical designs
• Written and verbal communication that builds alignment across engineering, research, legal, and product teams, up to senior leadership

Preferred qualifications

• Experience building privacy infrastructure or controls for machine learning or AI systems, including training data governance or 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, or enterprise customers on privacy risk, and defending your recommendations under challenge
• A history of growing engineers into Staff-level and more senior roles
• Contributions to open-source privacy tooling, privacy research, or industry standards