Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 6
Go @ 7
Python @ 7
Communication @ 4
CCPA @ 4
GDPR @ 4
Audit @ 4
Compliance @ 4
AI @ 4
Data Pipelines @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. The privacy engineering team builds world-class privacy into Anthropic's AI systems. This role is a seasoned individual contributor position focused on architecting and implementing privacy-preserving systems across AI training, inference, and data infrastructure.
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 Act) into actionable technical implementations and automated compliance controls
- Architect comprehensive data governance platforms for tracking data lineage, purpose limitation, and retention across distributed AI systems
- Lead technical privacy reviews and threat modeling for new AI models and features, identifying risks and architecting scalable mitigations
- Collaborate 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 engineers to build privacy-preserving features by default
- Design and implement privacy-preserving analytics and measurement systems that provide insights while protecting individual user privacy
- Research and evaluate emerging privacy technologies from academia and industry, contributing to open-source tools and AI privacy standards
- Act as consultant and advocate for privacy best practices across the organization
Requirements
- 10+ years of professional software engineering experience (excluding internships/co-ops)
- 5+ years of experience focused on privacy, security, or data protection
- Deep expertise in privacy engineering principles: privacy by design, data minimization, purpose limitation
- Strong programming skills in Python, Go, or similar languages with experience building production systems at scale
- Experience with privacy-enhancing technologies (differential privacy, homomorphic encryption, secure enclaves)
- Experience with federated learning and secure multi-party computation
- Proven track record of designing and implementing privacy infrastructure serving millions of users
- Expertise in data governance, classification, and lifecycle management systems
- Strong understanding of privacy regulations (GDPR, CCPA) and ability to translate legal requirements into technical solutions
- Experience conducting privacy reviews, threat modeling, and risk assessments
- BS/MS in Computer Science, Engineering, or equivalent practical experience
Compensation
- Annual Salary: $405,000 - $485,000 USD
Logistics
- Locations: San Francisco, CA; New York City, NY; Seattle, WA
- Location-based hybrid policy: staff are expected to be in one of Anthropic's offices at least 25% of the time
- Education: at least a Bachelor's degree in a related field or equivalent experience
- Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to assist (subject to role and candidate)
- Applications are reviewed on a rolling basis (no deadline)
Benefits
- Competitive compensation and benefits
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours and a pleasant office space
How we work
Anthropic emphasizes collaborative, large-scale AI research aligned with AI safety and steerability goals. Communication and cross-team partnership (research, product, infrastructure, legal) are important aspects of the role.