Staff + Sr. Software Engineer, Inference Deployment
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: - Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions - Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes - Extend deployment observability — dashboards and tooling that answer "what code is running in production," "where is my commit," and "what validation passed for this deploy" - Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism - Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity - Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement 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, Inference Deployment role.
- Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions
- Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes
- Extend deployment observability — dashboards and tooling that answer "what code is running in production," "where is my commit," and "what validation passed for this deploy"
- Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism
- Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity
- Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement
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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: 5111745008 · Confidence: 97/100 · Last checked: May 7, 2026
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