Staff+ Software Engineer, Privacy

Anthropic•Seattle, WA

4d•$405,000 - $485,000•Hybrid

About The Position

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 sit within our Data Infrastructure team, architecting privacy-preserving systems, leading the implementation of privacy-enhancing technologies across our infrastructure, and providing 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.

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

Nice To Haves

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
Experience with distributed systems and cloud infrastructure at scale
Experience serving as a technical lead on complex, multi-quarter projects
Contributions to open-source privacy tooling, privacy research, or industry standards
12+ years of experience in a software engineering role, including building and operating large-scale infrastructure
3+ years of experience leading large, complex projects as a technical lead

Responsibilities

Design and implement privacy-preserving architectures for AI training and inference systems operating at very large scale, using techniques e.g. 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