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
- We are seeking a Security Engineering Manager to lead our Secure Frameworks team, which builds high-leverage security frameworks and libraries that make secure development easy and prevent entire classes of vulnerabilities through careful design. You'll collaborate closely with teams and leaders across Anthropic to own critical security foundations, including cryptographic frameworks and secure serialization and authorization systems that empower teams to work securely without becoming security experts themselves.
Responsibilities
- Design frameworks and libraries that enable secure handling of sensitive data including model weights, customer data, and training datasets
- Own what you build completely; ensuring outstanding user experience, proactive monitoring, and responsive support
- Enable other teams to build their own security solutions by providing design pattern guidance and expanding security ownership beyond your team
- Partner with product, research, and infrastructure teams, as well as other Security teams to ensure frameworks integrate smoothly with lower-layer security controls
- Make strategic decisions about which frameworks to build based on security risk and translate into prioritized roadmaps
- Scale the team's impact from primarily supporting researchers today to enabling the broader product engineering organization as Anthropic grows
- Define success metrics around framework adoption and impact - the team succeeds when engineers naturally reach for Secure Frameworks' tools rather than building security solutions from scratch
- Manage and grow a team of engineers to deliver high-impact projects that balance security rigor with development velocity
You may be a good fit if you
- 5+ years managing security engineering teams with proven track record of team productivity
- 5+ years hands-on security and software engineering experience
- Deep expertise in securing complex architectures, threat modeling, and risk assessment with ability to evaluate security tradeoffs and make risk-based decisions
- Strong cross-functional collaboration skills, balancing security requirements with developer experience and velocity
- Clear and persuasive communicator in both writing and verbal settings
- Passionate about building diverse, high-performing teams and growing engineers in a fast-paced environment
- Low ego, high empathy, and have a track record as a talent magnet
- Experience working with technical internal customers
- Familiarity with AI safety concepts and frameworks
Deadline to apply: None. Applications will be reviewed on a rolling basis.
- 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.
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.
- 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.
