Staff Software Security Engineer

• Have 8-15+ years of software engineering experience implementing and maintaining critical systems at scale
• Strong software engineering skills in Python or at least one systems language (Go, Rust, C/C++)
• Experience implementing and operating critical systems at scale using DevOps and cloud automation practices such as infrastructure as code
• Hands-on network security engineering in cloud or on-prem environments, for example VPC design, cloud firewall policy, egress control, private service connectivity or network segmentation
• Working knowledge of Kubernetes security and networking, including network policy, service identity and container hardening
• Experience with threat modeling and risk assessment for networked and multi cloud systems
• Track record of driving engineering excellence through high standards, constructive code reviews and mentorship
• Clear communicator who can translate technical concepts across organizational levels and bring clarity and ownership to ambiguous technical problems
• Low ego, high empathy engineer who attracts talent and supports diverse, inclusive teams

• Experience supporting fast-paced startup engineering teams

• Secured Kubernetes networking at scale (CNI, network policy, service mesh or mTLS rollouts) or worked in high-assurance environments where controls had to be evidenced, not just deployed
• Worked on interconnects and private connectivity between cloud providers and physical data centers, including management-plane and out-of-band network security
• Built network traffic visibility or exfiltration detection systems
• Exposure to large-scale distributed training and what it demands of a network
• Led cross-functional security initiatives and navigated complex organizational dynamics
• Managing infrastructure through automated configuration and policy enforcement
• Hardening containerized applications and enforcing security policies

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

£255,000 - £325,000 GBP

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’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.

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.

• Build security for large-scale AI clusters across multiple clouds and bare-metal data centers, including IAM, network segmentation and encryption controls
• Design and implement network security controls: default-deny ingress and egress, segmentation between research, training and production environments, private connectivity across clouds and data centers, cloud firewall policy and mTLS with service identity
• Ship these controls as secure defaults using infrastructure as code and GitOps workflows, so new clusters, VPCs and data center links inherit the right boundaries
• Build visibility into network traffic and boundary drift, with detection for exfiltration paths and automated remediation where it is safe to do so
• Design secure-by-design development workflows and CI/CD pipelines across our services, with expertise in Kubernetes security, container orchestration and identity management
• Threat model and assess risk for complex multi cloud and networked environments, and join infrastructure networking, cluster and data center teams at design time so topology, routing and firewall decisions carry our security requirements from day one
• Mentor engineers and contribute to hiring and growth of the Security team, 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.

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.