3 hours ago
Seattle, WA, USA or San Francisco, CA, USASenior / Staff+
H1B Sponsor
Base Salary
$320k - $485k/yr
Responsibilities
- Design, build, and maintain distributed systems serving Claude to millions of users worldwide.
- Develop intelligent routing, load balancing, traffic management, autoscaling, and orchestration systems across thousands of accelerators and multiple cloud providers.
- Build and operate production-grade deployment pipelines for releasing new models reliably.
- Provide high-performance inference infrastructure for machine-learning research and support new model architectures and AI accelerator platforms.
- Analyze observability data, tune production performance, and manage multi-region deployments and geographic routing.
Requirements
- Significant software engineering experience, particularly with distributed systems.
- Experience with high-performance, large-scale distributed systems is preferred.
- Experience implementing and deploying machine-learning systems at scale is preferred.
- Experience with load balancing, request routing, or traffic management systems is preferred.
- Familiarity with LLM inference optimization, batching, and caching strategies is preferred.
- Experience with Kubernetes and cloud infrastructure such as AWS, GCP, and Azure is preferred.
- Proficiency in Python or Rust is preferred.
- A results-oriented, flexible approach and willingness to work beyond narrowly defined responsibilities.
- Interest in machine-learning systems and infrastructure and awareness of the societal impacts of the work.
Benefits
- Hybrid work policy requiring staff to work from an Anthropic office at least 25% of the time.
- Visa sponsorship is available, subject to role and candidate eligibility.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Categories
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.