Staff + Senior Software Engineer, Inference

Anthropic•Seattle, WA

10h•$320,000 - $485,000•Hybrid

About The Position

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 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.

Requirements

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
Enjoy pair programming (we love to pair!)
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

Nice To Haves

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

Responsibilities

Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide
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
Use observability data to tune and improve performance based on real-world production workloads