Lead Cloud Inference Engineer – Multi-Cloud Scale

EngineersOfAI; San Francisco (CA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Anthropic is seeking backend engineers to scale Claude across AWS, GCP, Azure, and future CSPs. You will design, build, and own services and infrastructure for cross-cloud inference, balancing performance, cost, and reliability.

Ideal candidates have deep distributed-systems experience, strong Python or Rust skills, and a proven ability to work across autonomous, cross-functional teams. This role emphasizes security, safety, and scalable operations across multiple cloud providers.

Qualifications

• Significant software engineering experience in large-scale distributed systems serving millions of users.
• Experience with at least one major cloud platform (AWS, GCP, or Azure).
• Familiarity with Kubernetes, Infrastructure as Code, or container orchestration.
• Strong collaboration across cross-functional teams and external partners.
• Fast learner who can ramp up on new technologies and provider ecosystems.
• Autonomous and owning problems end-to-end.

Responsibilities

• Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs.
• Collaborate with inference, product API, systems, and security teams and CSP partners.
• Build and evolve CI/CD pipelines to deploy model versions across cloud platforms.
• Design interfaces and tooling to enable cost-effective inference management across providers.
• Contribute to capacity planning, autoscaling, and workload routing strategies.
• Analyze observability data to identify performance bottlenecks and remediation opportunities.

Skills

Distributed systems

Cloud platforms

Kubernetes

Infrastructure as Code

Python

Rust

Cross-functional collaboration

Autonomy

Tools

Kubernetes

Infrastructure as Code

Job description

Anthropic is seeking backend engineers to scale Claude across AWS, GCP, Azure, and future CSPs. You will design, build, and own services and infrastructure for cross-cloud inference, balancing performance, cost, and reliability.

Ideal candidates have deep distributed-systems experience, strong Python or Rust skills, and a proven ability to work across autonomous, cross-functional teams. This role emphasizes security, safety, and scalable operations across multiple cloud providers.