About The Role
AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths โ 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. Reliability here is an emergent phenomenon that transcends any single teamโs boundaries, so someone has to zoom out and look at the whole picture. Thatโs us โ and it means few teams at Anthropic offer this kind of dynamic, crossโcutting exposure to the systems that matter most.
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 highโavailability serving infrastructure across multiple regions and cloud providers
- Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements
- Support the reliability of safeguard model serving โ critical for both site reliability and Anthropicโs safety commitments
You May Be a Good Fit If You
- Have strong distributed systems, infrastructure, or reliability backgrounds โ weโre looking for reliabilityโminded software engineers and SREs
- Are curious and brave โ comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you donโt have deep expertise yet
- Think holistically about how systems compose and where the seams are
- Can build lasting relationships across teams โ 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 donโt own
- Have excellent communication and collaboration skills โ youโll be partnering across the entire company
- Bring diverse experience โ the teamโs strength comes from people whoโve built product stacks, scaled databases, run massive distributed systems, and everything in between
Strong Candidates May Also
- Have been an SRE, Production Engineer, or in similar reliabilityโfocused roles on large scale systems
- Have experience operating largeโscale model serving or training infrastructure (over 1000 GPUs)
- Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium)
- Understand MLโspecific networking optimizations like RDMA and InfiniBand
- Have expertise in AIโspecific observability tools and frameworks
- Have experience with chaos engineering and systematic resilience testing
- Have contributed to openโsource infrastructure or ML tooling
Annual Salary
$325,000โ$485,000 USD
Logistics
Minimum education: Bachelorโs 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.
Locationโbased 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 do sponsor visas! However, we arenโt able to successfully sponsor visas for every role and every candidate. If we make you an offer, we will make every reasonable effort to get you a visa and retain an immigration lawyer to help with this.
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