Anthropic is seeking a Staff+ Software Engineer, Privacy to establish and lead the privacy engineering function. The role involves architecting privacy-preserving systems for large-scale AI training and inference, embedding privacy controls across products, translating regulatory requirements into technical solutions, and building foundational privacy infrastructure. The position requires deep expertise in privacy principles, scalable infrastructure, and regulatory compliance, and offers competitive compensation with extensive benefits.
Staff+ Software Engineer, Privacy at Anthropic
On-site - San Francisco, CA; New York City, NY; Seattle, WA
More jobs at AnthropicSalary
USD 405,000 - 485,000
Requirements
Skills
- Experience applying privacy engineering principles in production systems, including privacy by design, data minimization, and purpose limitation
- Proficiency in Python, Go, or similar languages, with experience building and operating production systems at scale
- Experience designing and implementing privacy infrastructure for systems with a large user base
- Experience with data governance, classification, or data lifecycle management systems
- Understanding of privacy regulations such as GDPR and CCPA, and the ability to translate legal requirements into technical designs
- Experience conducting privacy reviews, threat modeling, or risk assessments
- Written and verbal communication skills sufficient to drive alignment across engineering, research, legal, and product teams
Responsibilities
- Design and implement privacy-preserving architectures for AI training and inference systems operating at very large scale, using techniques such as differential privacy, federated learning, and secure multi-party computation
- Partner with researchers to implement privacy-preserving training methods that protect user data while maintaining model quality
- Build foundational privacy infrastructure, including automated data discovery, classification, access controls, audit logging, and lifecycle management
- Translate regulatory requirements (e.g., GDPR, CCPA, HIPAA, the 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, identifying risks and designing scalable mitigations
- Partner with product and infrastructure teams to 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 surface useful insights without exposing individual user data
- Evaluate emerging privacy technologies from academia and industry, and contribute to open-source tooling and AI privacy standards
- Advise on and advocate for privacy practices as a core part of how we approach AI safety
Technologies
PythonGodifferential privacyfederated learningsecure multi-party computationprivacy-enhancing technologiesprivacy infrastructuredata governancedata classificationdata lifecycle managementprivacy reviewsthreat modelingprivacy toolkits
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