About this role
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
Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.
The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.
Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems.
Key responsibilities
• Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide
• Develop resilient, flexible systems that adapt in real time to real world events
• Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators
• Maximize compute efficiency across the fleet by autoscaling and orchestrating production, research, and experimental workloads
• Build and operate production-grade deployment pipelines for releasing new models to users
• Provide high-performance inference infrastructure that enables researchers to develop next-generation models
• Integrate new AI accelerator platforms and support inference for new model architectures
Minimum qualifications
• Significant software engineering experience, particularly with distributed systems
• Results-oriented, with a bias towards flexibility and impact
• Willingness to pick up slack, even if it goes outside your job description
• Desire to learn more about machine learning systems and infrastructure
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs
• Care about the societal impacts of your work
Preferred qualifications
• Experience with high-performance, large-scale distributed systems
• Experience implementing and deploying machine learning systems at scale
• Experience with load balancing, request routing, or traffic management systems
• Familiarity with LLM inference optimization, batching, and caching strategies
• Experience with Kub