The Cloud Inference team scales and optimizes Claude to serve massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers. 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.
Staff + Sr. Software Engineer, Cloud Inference Launch Engineering at Anthropic
Hybrid - San Francisco, CA, United States
More jobs at AnthropicSalary
USD 320,000 - 485,000
Requirements
Skills
- Strong interest in LLM serving; prior inference or ML experience is not required
- Significant software engineering experience with high‑performance, large‑scale distributed systems serving millions of users
- Track record of building automation or test infrastructure that measurably improved release velocity or reliability
- Experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure) with exposure to Kubernetes, Infrastructure as Code, or container orchestration
- Thrives in cross‑functional collaboration with both internal teams and external partners
- Fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
- Highly autonomous and takes ownership of problems end‑to‑end, including work that falls outside the job description
- Proficiency in Python or Rust
Responsibilities
- Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first‑party platform
- Work with the core inference team to bring new inference features (e.g., structured sampling, prompt caching, and more) to cloud platforms, owning the platform‑specific integration that gets them to production
- Identify and dive deep on the gaps that make inference behave differently across first‑party and CSPs—config drift, observability, deployment patterns, hard cross‑platform bugs—and fix them at the source rather than building platform‑specific workarounds
- Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production
- Drive down merge‑to‑production cycle time by making validation faster, more parallel, and cost‑effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability
- Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real‑world production workloads
Technologies
PythonRustKubernetesInfrastructure as CodeContainer orchestrationCI/CDCloud platforms (AWS, GCP, Azure)
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