Staff Software Engineer, AI Reliability Engineering

The shape of it

In the posting’s own words

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.

What it asks for · 7

• 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.

Also a plus

• 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.

What the job covers

• 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.

Tools and skills named

Words the posting leans on

• systems10×
• reliability6×
• serving6×
• infrastructure5×
• claude4×
• experience4×
• incident4×
• critical3×
• model serving3×
• accelerators2×
• aire2×
• back2×
• depends2×
• design2×
• distributed systems2×
• engineer2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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