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Staff + Sr. Software Engineer, Cloud Inference Launch Engineering

Anthropic· Software Engineering - Infrastructure
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San Francisco, CA, Seattle, WAfull-timePosted 3w ago

About the role

Role focused on validating, launching, and operating Claude inference on major cloud platforms (AWS, GCP, Azure) with emphasis on end-to-end integration, CI/CD, observability, and reliability across CSPs.

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

Benefits

  • generous vacation and parental leave
  • optional equity donation matching
  • flexible working hours
  • office space for collaboration
Last updated Sep 30, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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