Staff+ Software Engineer, Privacy

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, How do you know if you have a disability? A disability is a condition that substantially limits one or more of your “major life activities.” If you have or have ever had such a condition, you are a person with a disability. Disabilities include, but are not limited to:

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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 and 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, Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates’ AI Usage: Learn about our policy for using AI in our application process.

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., 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., We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We’re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences., As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows:

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