Staff+ Software Engineer, Privacy

About the role

Anthropic is building frontier AI systems that process massive amounts of sensitive data. As one of the first dedicated privacy engineers, you will help create a privacy‑first culture by designing and implementing privacy‑preserving architectures that are baked into the core of our AI training and inference pipelines.

Key responsibilities

• Design and implement privacy‑preserving architectures for AI training and inference at very large scale using differential privacy, federated learning, and secure multi‑party computation.
• Partner with researchers to integrate privacy‑preserving training methods while maintaining model quality.
• Build foundational privacy infrastructure such as automated data discovery, classification, access controls, audit logging, and lifecycle management.
• Translate regulatory requirements (GDPR, CCPA, HIPAA, EU AI Act) into technical implementations and automated compliance controls.
• Architect data‑governance systems for tracking data lineage, purpose limitation, and retention across distributed AI systems.
• Lead privacy reviews and threat modeling for new models and features, designing scalable mitigations.
• Embed privacy controls into Claude’s inference systems, user interfaces, and data pipelines.
• Develop privacy‑engineering toolkits and frameworks that enable other engineers to build privacy‑preserving features by default.
• Design privacy‑preserving analytics and measurement systems that provide insights without exposing individual user data.
• Evaluate emerging privacy technologies and contribute to open‑source tooling and AI privacy standards.

Required profile

• Proven experience applying privacy‑engineering principles to large‑scale systems.
• Strong background in distributed systems, data infrastructure, and AI safety.
• Ability to work autonomously, influence cross‑functional teams, and drive privacy initiatives from concept to production.

Required skills

• Differential privacy
• Federated learning
• Secure multi‑party computation
• GDPR, CCPA, HIPAA, EU AI Act compliance
• Automated data discovery and classification
• Access controls, audit logging, data lifecycle management
• Data lineage and governance
• Threat modeling and privacy reviews
• Privacy‑engineering toolkits and frameworks
• Privacy‑preserving analytics and measurement