Anthropic is hiring a Staff + Senior Software Engineer in the Inference team to build and maintain distributed systems that serve Claude to millions of users worldwide. The role involves designing intelligent routing, load balancing, autoscaling, and production deployment pipelines, while also enabling research teams with high‑performance inference infrastructure across diverse AI accelerators and cloud platforms. The position is hybrid, requiring presence in one of our U.S. offices at least 25% of the time, and offers a competitive salary, benefits, equity donation matching, and generous leave policies.
Staff + Senior Software Engineer, Inference at Anthropic
Hybrid - San Francisco, CA; New York City, NY; Seattle, WA
More jobs at AnthropicSalary
USD 320,000 - 485,000
Requirements
Skills
- 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
- 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
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
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
KubernetesAWSGCPAzurePythonRust
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