Staff + Sr. Software Engineer, Cloud Inference
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.
What this role actually needs.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Responsibilities: - Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models - Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms - Design 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 and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads - Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region Company context: Anthropic is an AI safety company building Claude, a frontier-model assistant for developers, enterprises, and consumers.
Day-to-day expectations
Anthropic lists these responsibilities for the Staff + Sr. Software Engineer, Cloud Inference role.
- Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
- Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms
- Design 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 and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads
- Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region
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Current extracted signal: Open to TN, H-1B, and OPT candidates already in the United States. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.
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On-site roles in New York City should be compared against commute, local salary bands, and nearby employer demand.
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Source: job-boards.greenhouse.io · Source ID: 5107466008 · Confidence: 97/100 · Last checked: May 7, 2026
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