Staff + Senior Software Engineer, Inference
AnthropicAbout 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.
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.
Key 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
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
- 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 ab
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