The Inference team at Anthropic builds and maintains the critical systems that serve Claude to millions of users worldwide. This role focuses on highly performance‑sensitive distributed systems, including intelligent request routing, load balancing, autoscaling, and production‑grade deployment pipelines. The successful candidate will work on scaling and networking challenges across diverse AI accelerators and cloud platforms, ensuring efficient compute usage and robust infrastructure for both research and production workloads.
Staff Software Engineer, Inference en Anthropic
Híbrido - London, UK
Más vacantes en AnthropicSalary
GBP 325,000 - 390,000
Requirements
Skills
- 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, bias towards flexibility and impact
- Willingness to pick up slack
- Desire to learn more about machine learning systems and infrastructure
- Thrive in environments where technical excellence directly drives business results and research breakthroughs
- Care about the societal impacts of your work
- 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)
- Bachelor’s degree or equivalent education/training
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
Technologies
PythonRustKubernetesAWSGCPAzureGPUsTPUsAI accelerator platforms
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