Staff+ Software Engineer, Privacy
San Francisco, CA | New York City, NY | Seattle, WA
Posted 7d ago
About the job
Anthropic is seeking a foundational privacy engineer to establish the privacy engineering function and shape how privacy is designed into their AI systems from the ground up. This role sits within the Data Infrastructure team, focusing on architecting privacy-preserving systems, implementing privacy-enhancing technologies, and providing technical leadership across engineering, research, and product teams. You will work at the intersection of privacy engineering, AI safety, and distributed systems, tackling novel problems with high autonomy and broad influence.
Responsibilities
- Design and implement privacy-preserving architectures for AI training and inference systems using techniques like 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, 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 inference systems, user interfaces, and data pipelines.
- Develop privacy engineering toolkits and frameworks to 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 and contribute to open-source tooling and AI privacy standards.
- Advise on and advocate for privacy practices as a core part of AI safety.
Requirements
- 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 build alignment across engineering, research, legal, and product teams.