Job Details

About the role

Anthropic is seeking an experienced engineering leader to manage one of our Reliability Engineering teams. This team includes Software Engineers and Systems Engineers focused on defining and achieving reliability metrics for Anthropic's critical serving systems. As a manager, you'll lead the team that's significantly improving reliability for Anthropic's services while pioneering the use of modern AI capabilities to reengineer how we approach reliability engineering. This leadership role is critical to Anthropic's mission to bring groundbreaking AI technologies to benefit humanity in a safe and reliable way.

Responsibilities:

• Lead and grow a team of reliability engineers responsible for large language model serving.
• Drive the development of Service Level Objectives that balance availability/latency with development velocity across the organization
• Oversee the design and implementation of comprehensive monitoring systems for availability, latency and other critical metrics
• Guide your team in architecting high-availability language model serving infrastructure capable of supporting millions of external customers and high-traffic internal workloads
• Lead the strategy for automated failover and recovery systems across multiple regions and cloud providers
• Establish and manage incident response processes for critical AI services, ensuring your team drives rapid recovery and systematic improvements
• Direct cost optimization initiatives for large-scale AI infrastructure, with focus on accelerator (GPU/TPU/Trainium) utilization and efficiency
• Partner with cross-functional teams to align reliability engineering efforts with broader company objectives
• Build a strong engineering culture focused on reliability, operational excellence, and innovation

You may be a good fit if you:

• Have experience managing and scaling reliability or infrastructure engineering teams
• Possess deep technical knowledge of distributed systems observability and monitoring at scale
• Understand the unique challenges of operating AI infrastructure and can guide technical decisions
• Have successfully implemented SLO/SLA frameworks and can drive adoption across organizations
• Bring experience with both traditional infrastructure metrics and AI-specific performance indicators
• Can effectively lead technical discussions while translating between ML engineers and infrastructure teams
• Have excellent leadership and communication skills, with ability to influence at all levels
• Demonstrate strong hiring and talent development capabilities

Strong candidates may also:

• Have managed teams operating large-scale model training or serving infrastructure (>1000 GPUs)
• Bring hands-on experience with ML hardware accelerators (GPUs, TPUs, Trainium, etc.)
• Understand ML-specific networking optimizations and their operational implications
• Have led teams through major reliability transformations or infrastructure migrations
• Possess experience building reliability engineering practices from the ground up
• Have contributed to or led open-source infrastructure or ML tooling initiatives
• Demonstrate thought leadership in the reliability engineering community