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

Anthropic is working on frontier AI systems that handle sensitive information at enormous scale. How we protect that data — and how we build privacy into our systems rather than bolting it on afterward — is central to our mission of building AI that is safe and beneficial. This is a foundational role.

As one of our first dedicated privacy engineers, you will help establish the privacy engineering function at Anthropic and shape how privacy is designed into our AI systems from the ground up. You'll architect privacy-preserving systems, lead the implementation of privacy-enhancing technologies across our infrastructure, and provide technical leadership on privacy across engineering, research, and product teams. You'll work at the intersection of privacy engineering, AI safety, and distributed systems, solving problems that don't yet have established answers.

This is a senior individual contributor role with high autonomy and broad influence.

Key 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

Minimum Qualifications

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

Preferred Qualifications

Hands-on experience with privacy-enhancing technologies (e.g., differential privacy, homomorphic encryption, secure enclaves, secure multi-party computation) Experience building privacy infrastructure or controls for machine learning or AI systems Experience establishing a privacy engineering practice, or being an early hire in a function

Staff+ Software Engineer, Privacy