Staff Software Engineer, Inference
Dublin, IE
Posted 16d ago
About the job
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.
Responsibilities
- Work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research.
- Design intelligent routing algorithms that optimize request distribution across thousands of accelerators.
- Autoscale compute fleets to dynamically match supply with demand across production, research, and experimental workloads.
- Build production-grade deployment pipelines for releasing new models to millions of users.
- Integrate new AI accelerator platforms to maintain hardware-agnostic competitive advantage.
- Contribute to new inference features such as structured sampling and prompt caching.
- Support inference for new model architectures.
- Analyze observability data to tune performance based on real-world production workloads.
- Manage multi-region deployments and geographic routing for global customers.
Requirements
- Familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing.
- Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.
- High-performance, large-scale distributed systems experience.
- Experience implementing and deploying machine learning systems at scale.
- Experience with load balancing, request routing, or traffic management systems.
- Experience with LLM inference optimization, batching, and caching strategies.
- Experience with Kubernetes and cloud infrastructure (AWS, GCP).
- Experience with Python or Rust.
- Significant software engineering experience, particularly with distributed systems.
- Results-oriented, with a bias towards flexibility and impact.
- Ability 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.