Job Specifications
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 Team
The Security Engineering team's mission is to safeguard our AI systems and maintain the trust of our users and society at large. Whether we're developing critical security infrastructure, building secure development practices, or partnering with our research and product teams, we are committed to operating as a world-class security organization and keeping the safety and trust of our users at the forefront of everything we do. Responsibilities
Build security for large-scale AI clusters, implementing robust cloud security architecture including IAM, network segmentation, and encryption controls
Design secure-by-design workflows, secure CI/CD pipelines across our services, help build secure cloud infrastructure, with expertise in various cloud environments, Kubernetes security, container orchestration and identity management
Ship and operate secure, high-reliability services using Infrastructure-as-Code (IaC) practices and GitOps workflows
Apply deep expertise in threat modeling and risk assessment to secure complex cloud environments
Mentor engineers and contribute to hiring and growth of the Security team You may be a good fit if you have
5-15+ years of software engineering experience implementing and maintaining critical systems at scale
Bachelor's degree in Computer Science/Software Engineering or equivalent industry experience
Strong software engineering skills in Python or at least one systems language (Go, Rust, C/C+)
Experience managing infrastructure at scale with DevOps and cloud automation best practices
Track record of driving engineering excellence through high standards, constructive code reviews, and mentorship
Proven ability to lead cross-functional security initiatives and navigate complex organizational dynamics
Demonstrated success in bringing clarity and ownership to ambiguous technical problems
Strong systems thinking with ability to identify and mitigate risks in complex environments
Low ego, high empathy engineer who attracts talent and supports diverse, inclusive teams
Experience supporting fast-paced startup engineering teams
Passionate about AI safety and alignment, with keen interest in making AI systems more interpretable and aligned with human values Strong candidates may also
Design and harden CI/CD pipelines against supply chain attacks through isolated environments, signed attestations, dependency verification, and automated policy enforcement
Build secure development workflows through hardened remote environments
Implement network segmentation and access controls in cloud environments
Manage infrastructure through automated configuration and policy enforcement
Harden containerized applications and enforce security policies Compensation
The expected base compensation for this position is $320,000 - $405,000 USD. Our total compensation package for Full time employees includes equity, benefits, and may include incentive compensation. Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
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.
Visa sponsorship: We sponsor visas. While we cannot sponsor every role for every candidate, we will make every reasonable effort to secure a visa if we make an offer.
EEO statement: As set forth in Anthropic's Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
About the Company
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale.
Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
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