Staff Software Engineer, Inference

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.

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. 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 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

• Proficiency in Python or Rust
• Software engineering experience building and operating distributed systems in production
• Working knowledge of containerized infrastructure (e.g., Kubernetes) and at least one major cloud platform (AWS, GCP, or Azure)
• 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

• Significant experience with high-performance, large-scale distributed systems
• Experience implementing and deploying machine learning systems at scale
• Experience building load balancing, request routing, or traffic management systems
• Familiarity with LLM inference optimization, batching, and caching strategies
• Deep experience operating Kubernetes and cloud infrastructure at scale
• Experience with AI accelerator platforms (GPUs, TPUs, or emerging hardware)

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

Deadline to apply

None. Applications will be reviewed on a rolling basis.

Compensation

The annual compensation range for this role is listed below.