Staff Software Engineer, AI Reliability

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 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. Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting 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 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. You may be a good fit if you: - 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 - Bring diverse experience -- the team's strength comes from people who've 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 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. 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: $325,000—$485,000 USD Logistics 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 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 aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to

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: Correlates with internal job level requirements for the position. Visa sponsorship: Yes, the company sponsors visas. Strong candidates may have previously worked as an SRE, Production Engineer, or in similar reliability-focused roles on large-scale systems. Experience operating large-scale model serving or training infrastructure (>1000 GPUs). Experience with ML hardware accelerators (GPUs, TPUs, Trainium). Understanding of ML-specific networking optimizations like RDMA and InfiniBand. Expertise in AI-specific observability tools and frameworks. Experience with chaos engineering and systematic resilience testing. Contributions to open-source infrastructure or ML tooling.