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# Staff + Sr. Software Engineer, Scaling

- **Company:** Anthropic Limited
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $320,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Distributed Systems, Python (Programming Language), Routing, Software Engineering, Network Routing, Computer Networking Systems, Google Cloud, Load Balancing, Delivery Pipeline, Caching, Kubernetes, Hardware Acceleration, Machine Learning Operations, Claude, Model Inference, Multiplatform
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/staff-sr-software-engineer-scaling-san-francisco-ca--66c08517-d6f3-45af-85bc-a522beadb6c8

## About the Role

Software Engineering, Artificial Intelligence (AI), Vehicle Fleets, Distributed Computing, Cloud Computing, Multiplatform/Cross-Platform, Network Systems, Network Routing, Systems Maintenance, Load Balancing, * 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, Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

## Description

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