Staff + Sr. Software Engineer, Scaling

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 scaling 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 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 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 Kubernetes and cloud infrastructure (AWS, GCP, Azure)

Proficiency in Python or Rust

Representative projects

Designing intelligent routing algorithms that optimize request distribution across many accelerators in different environments

Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads

Building production-grade deployment pipelines for releasing new models to millions of users reliably

Contributing to new inference features

Supporting inference for new model architectures

Analyzing observability data to tune performance based on real-world production workloads

Managing multi-region deployments and geographic routing for global customers

The annual compensation range for this role is listed below. 

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissi …