Engineering Manager, Cloud Safety

Engineering Manager, Cloud Safety

San Francisco, CA | Seattle, WA

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.

We are seeking an experienced Engineering Manager to lead the Cloud Safety team. You will lead your team to scale and optimize Claude to serve the massive audiences of developers and enterprise companies using Claude via Cloud Service Providers (CSPs). You will own safety end-to-end for these services, including API, inference, classifiers, fraud detection, data management and operations. Your team will ensure our Claude is used safely on all CSPs.

Your work will increase the scale at which Anthropic operates and accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms.

• Set technical strategy and oversee development of safety features across all Claude CSP surfaces
• Collaborate across teams and companies to deeply understand product, infrastructure, operations and capacity needs, identifying potential solutions to support frontier LLM serving
• Work closely with cross-functional stakeholders across companies to align on goals and drive outcomes
• Create clarity for the team and stakeholders in an ambiguous and evolving environment
• Take an inclusive approach to hiring and coaching top technical talent, and support a high performing team
• Design and run processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice

• Have 8+ years of experience in high-scale, high-reliability software development, particularly infrastructure or capacity management
• Have 3+ years of engineering management experience
• Experience recruiting, scaling, and retaining engineering talent in a high growth environment
• Have experience scaling products, resources and operations to accommodate rapid growth
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships and strategy with stakeholders across engineering, product, finance, and policy
• Have experience working with external partners to align goals and deliver impact
• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space
• Have excellent written and verbal communication skills
• Demonstrated success building a culture of belonging and engineering excellence
• Are motivated by developing AI responsibly and safely
• Are willing and able to travel frequently between Seattle and the SF Bay Area

• Experience with identity and access controls — KYC/KYB, verification flows, tiered access
• Experience with detecting and mitigating platform abuse at scale — account fraud, bot traffic, model extraction
• Experience with trust & safety, security, and/or legal/compliance
• Experience or interest in machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Experience with deployment and capacity management automation

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: $405,000 - $485,000 USD

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…