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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer, AI Reliability Engineering - **Company:** Anthropic's Mission - **Location:** UK - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Databases, Distributed Systems, InfiniBand, Open Source Technology, Remote Direct Memory Access, Reliability Engineering, Graphics Processing Unit (GPU), Grafana, AI Platforms, Hardware Acceleration, Machine Learning Operations - **Published:** May 31, 2026 - **Apply:** https://www.apply4u.co.uk/jobs/x/37804535/ ## About the Role 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. ## Description Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it. 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 providers 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. ## Related Videos - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [The Gashlycrumb Tinies of AI Networking You Must Know (or Languish!)](https://www.wearedevelopers.com/videos/2067-the-gashlycrumb-tinies-of-ai-networking-you-must-know-or-languish) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Staying Safe in the AI Future](https://www.wearedevelopers.com/videos/521-staying-safe-in-the-ai-future) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Trustworthy AI Starts at Deployment: 5 Checks Before You Ship](https://www.wearedevelopers.com/magazine/753-trustworthy-ai-starts-at-deployment-5-checks-before-you-ship) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)