Anthropic is working on frontier AI systems that handle sensitive information at enormous scale. This foundational senior individual contributor role establishes the privacy engineering function, architects privacy-preserving systems, leads the implementation of privacy‑enhancing technologies across infrastructure, and provides technical leadership across engineering, research, and product teams.
Staff+ Software Engineer, Privacy na Anthropic
Híbrido - San Francisco, CA | New York City, NY | Seattle, WA
Ver mais vagas na 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 build alignment across engineering, research, legal, and product teams
- 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
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
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 ComputationHomomorphic EncryptionSecure EnclavesData GovernancePrivacy RegulationsMachine LearningAI SystemsDistributed SystemsCloud Infrastructure
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