Staff + Sr. Software Engineer, Cloud Inference na Anthropic

Híbrido - San Francisco, CA

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The Cloud Inference team scales and optimizes Claude to serve massive audiences across AWS, GCP, Azure, and future CSPs. We own the end‑to‑end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day‑to‑day operations. Engineers drive multiple major revenue streams while optimizing compute resources at massive scale, building product‑oriented backend services, and making architectural decisions that keep us reliable and cost‑effective at massive scale.

Salary

USD 320,000 - 485,000

Requirements

Skills

  • Significant software engineering experience with high-performance, large-scale distributed systems serving millions of users
  • Experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure)
  • Exposure to Kubernetes, Infrastructure as Code, or container orchestration
  • Curiosity about LLM serving (prior inference or ML experience not required)
  • Ability to thrive in cross‑functional collaboration with internal teams and external partners
  • Experience working with external partners to align goals and deliver impact
  • Fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
  • Highly autonomous and able to take ownership of end‑to‑end problems
  • Direct experience scaling infrastructure or products across multiple CSPs and navigating networking, security, privacy, billing, and managed service differences
  • Hands‑on experience with capacity management, cost optimization, or resource planning at scale
  • Solid understanding of multi‑region deployments, geographic routing, and global traffic management
  • Proficiency in Python or Rust

Responsibilities

  • Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
  • Work cross‑functionally with internal inference, product API, systems, and security teams, and with CSP partners to stand up the full serving stack on new cloud platforms, resolve operational issues, and influence provider roadmaps
  • Build and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions
  • Design interfaces and tooling abstractions across CSPs that enable cost‑effective inference management, scale across providers, and reduce per‑platform complexity
  • Contribute to capacity planning, autoscaling, and workload routing strategies that match supply with demand and direct requests to the most cost‑effective accelerator and region
  • Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real‑world production workloads

Technologies

AWSGCPAzureKubernetesInfrastructure as CodeContainer orchestrationPythonRustCapacity managementCost optimizationMulti‑region deploymentsGeographic routingGlobal traffic management

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