Staff+ Software Engineer, Privacy
Anthropic - San Francisco, CA | New York City, NY | Seattle, WA
Posted Mar 19, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Staff+ Software Engineer, Privacy San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We're seeking an exceptional seasoned Privacy Engineer to join our growing privacy engineering team and help scale our privacy infrastructure as we navigate the transformative AI landscape. As one of our first dedicated privacy engineers, you'll have an outsized impact in shaping how Anthropic builds world-class privacy into our AI systems from the ground up. This is a seasoned individual contributor role where you'll provide technical and cultural leadership, architect innovative privacy-preserving systems, and drive implementation of cutting-edge privacy technologies across our AI infrastructure. You'll work at the intersection of privacy engineering, AI safety, and distributed systems to solve novel challenges in protecting user data at scale. Responsibilities: - Design and implement privacy-preserving architectures for AI training and inference systems handling billions of conversations, leveraging differential privacy, federated learning, and secure multi-party computation - Partner with AI researchers to implement privacy-preserving training methodologies that maintain model quality while protecting user data - Build foundational privacy infrastructure including automated data discovery, classification, access controls, audit logging, and lifecycle management systems - Translate complex regulatory requirements (GDPR, CCPA, HIPAA, EU AI
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