Senior Staff Software Engineer, AI Inference & Scale

Anthropic

Seattle (WA)

On-site

USD 180,000 - 280,000

Full time

5 days ago
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Benefits offered by this job

Comprehensive health, dental, and eye/
Relocation support
Annual education stipend
Home office stipend
Daily meals and snacks in the office

Job summary

Anthropic is seeking an experienced engineer to design, build, and operate the high-performance inference infrastructure that serves Claude globally. You will own distributed systems across multiple accelerators and cloud platforms, implementing intelligent routing, autoscaling, and resilient deployment pipelines.

The role focuses on maximizing compute efficiency, supporting both production and research workloads, and enabling researchers to push the boundaries of model architectures at scale in

Qualifications

  • Significant software engineering experience with distributed systems.
  • Proficiency in Python or Rust.
  • Experience with load balancing and traffic management.
  • Experience deploying ML systems at scale.
  • Familiarity with Kubernetes and cloud infrastructure.

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 across accelerators and cloud providers.
  • Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestration.

Skills

Distributed systems
Python or Rust
Kubernetes
Cloud infra (AWS, GCP, Azure)
LLM inference optimization

Tools

Kubernetes
AWS
GCP
Azure

Job description

Anthropic is seeking an experienced engineer to design, build, and operate the high-performance inference infrastructure that serves Claude globally. You will own distributed systems across multiple accelerators and cloud platforms, implementing intelligent routing, autoscaling, and resilient deployment pipelines.

The role focuses on maximizing compute efficiency, supporting both production and research workloads, and enabling researchers to push the boundaries of model architectures at scale in

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