About the job
AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.
Responsibilities
Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity
Design and implement monitoring and observability systems across the token path
Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud provider
Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements
Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.
Qualifications
Minimum
Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs
Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet
Think holistically about how systems compose and where the seams are
Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions
Care about users and feel ownership over outcomes, even for systems you don't own
Have excellent communication and collaboration skills -- you'll be partnering across the entire company
Preferred
Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems
Have experience operating large-scale model serving or training infrastructure (>1000 GPUs)
Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium)
Understand ML-specific networking optimizations like RDMA and InfiniBand
Have expertise in AI-specific observability tools and frameworks
Have experience with chaos engineering and systematic resilience testing
Have contributed to open-source infrastructure or ML tooling.