Description
About the role
Anthropic's Inference team is responsible for building and scaling 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.
Key responsibilities
- Design, build, and maintain distributed systems serving Claude to millions of users worldwide
- Develop resilient, flexible systems adapting in real-time to real-world events
- Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators and multiple cloud providers
- Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestrating production, research, and experimental workloads across multiple cloud providers
- Build and operate production-grade deployment pipelines for releasing new models to users
- Provide high-performance inference infrastructure for 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 drives 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 Kubernetes and cloud infrastructure (AWS, GCP, Azure)
- Proficiency in Python or Rust
The annual compensation range for this role is $320,000-$485,000 USD.