
Software Engineering Manager, Network Security
Primary stack
Nice to have's
Job description
About the Position
We're seeking a Software Engineering Manager, Network Security to lead our team in San Francisco, CA and New York City, NY.
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 Role
The Network Security team's remit is to secure Anthropic's networks across our cloud providers and our bare-metal AI data centers. We own egress and ingress controls, network segmentation, private connectivity between environments, and encryption in transit. We design, build, and maintain these controls, and we write the software that deploys, enforces, and monitors them. These controls protect our model weights, customer data, and internal assets.
We are hiring an Engineering Manager to lead this team. Anthropic is adding data center sites and cloud regions quickly, and the team will grow with that work. In this role you will set the team's charter, practices, and priorities. You will partner with our infrastructure networking, cluster, and data center teams so security is built into network designs from the start. The goal is secure-by-default networking that lets research and product teams use compute in any environment without slowing them down.
Key Responsibilities
- Own the network security roadmap: threat model from the management plane through Kubernetes and cloud interconnects, decide what to build, and deliver it
- Be able to reason about the design and implementation of default-deny ingress and egress, segmentation between research, training, and production, private connectivity, and mTLS/service identity across every environment
- Ship these as secure-by-default primitives so new clusters, VPCs, and data center links inherit the right boundaries without security on the critical path
- Join networking, cluster, and data center teams at design time so topology, routing, and firewall decisions carry our invariants from day one
- Build visibility into traffic and boundary drift, with detection for exfiltration paths and automated remediation where safe
- Own the network security evidence for our security goals and compliance: the invariants hold, and you can prove it
- Hire, grow, and retain senior security software engineers; own their career development, and set the practices the team runs on as it scales
Minimum Requirements
- 8+ years of front line management experience
- Low ego, high empathy; has built diverse teams and has former reports who would work for them again
- Experience managing infrastructure or security engineering teams, including hiring, developing, and managing senior and staff engineers
- Has grown engineers into staff or lead roles, and has made the call to move someone out when coaching didn't work
- Kept a team intact and delivering through reorgs, charter changes, or leadership turnover
- Experience owning a security or infrastructure domain end to end: setting multi-year strategy, delivering year-long cross-organizational work with little direction, and answering to executives for its risk and tradeoffs
- Scaled a team and its systems together through hypergrowth, where last quarter's architecture was already too small
- Hands-on network security engineering in cloud and/or on-prem environments: VPC design, interconnects, private service connectivity, cloud firewall policy, egress control, physical segmentation, and out-of-band/management-plane security
- Threat modeling and risk assessment for networked systems, with the judgment to tell which controls reduce risk and which just look good on a diagram
- Secured Kubernetes networking at scale, or worked in high-assurance environments where controls had to be evidenced, not just deployed
Preferred Qualifications
- Built or rebuilt a hiring loop and held the bar while adding headcount quickly
- Exposure to large-scale distributed training and what it demands of a network
- Solved end-to-end networking problems directly with internal users, cloud account teams, and hardware or colo vendors
- Has taken infrastructure from zero to one and then to maturity: built the first version where nothing existed, then run and hardened it into a system other teams depend on at production-network reliability
- Pragmatic and steady: cares about unblocking research and product as much as a clean architecture, and brings patience and judgment to opinionated senior engineers
- Clear and persuasive communicator in writing and in person
- Familiar with AI security and safety concepts
We Offer
- Annual Salary: $405,000-$485,000 USD
- Competitive compensation and benefits
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours
- Lovely office space in San Francisco
Logistics
- 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.
- Visa sponsorship: We sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
How We're Different
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.
Come Work with Us!
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.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links-visit anthropic.com/careers directly for confirmed position openings.
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Label Your Data provides secure and high quality data annotation services for Computer Vision or NLP applications since 2020. We help Data Scientists and AI Engineers streamline the dataset labeling and focus on model development.About Label Your Data:— Founded in 2020;— 200+ annotation experts in Europe, LATAM and Africa;— 98%+ annotation accuracy benchmark;— 55 supported languages;— PCI/DSS certified;— ISO/IEC 27001:2013 certified;— GDPR, CCPA and HIPAA-compliant;— HQs in North America, EU and Asia.Our experience spans over 20 industries including Automotive, Robotics, Agriculture, E-commerce, Retail, Healthcare, Manufacturing, Fintech and Insurance.Our core services include but are not limited to:— Image & video annotation: 2D boxes, OCR, object/action detection, polygons, key points, semantic segmentation, 3D cuboids;— Text annotation: classification, NER, intent & sentiment analysis, prompts creation, Q&A pairs generation;— Audio annotation: transcription, sentiment recognition;— Sensor data annotation;— Data classification & categorization;— Data entry;— Data collection;— Model validation.
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