Staff + Senior 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. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic serves Claude to millions of users across GPUs, TPUs, and Trainium — and every model update must reach production safely, quickly, and without disrupting service. The Launch Engineering team's mandate is to make inference deployment boring and unattended. As a Software Engineer on Launch Engineering, you'll design and build the deployment infrastructure that moves inference code from merge to production. This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic, so your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, and the system must adapt continuously. You'll build systems that navigate these constraints — orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production. Key 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 accelerator chips, minimizing disruption to serving capacity - Evolve self-service model onboarding so new models can be added to the continuous deployment pipeline without Launch Engineering involvement - Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems Minimum qualifications - Strong software engineering skills, including experience designing systems that manage complex state machines and multi-stage pipelines - Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration - Experience building deployment, release, or delivery infrastructure where resource constraints (fleet capacity, network bandwidth, hardware availability, coordinated rollout windows) shape the design - A track record of building automation that measurably improves deployment velocity and reliability - Comfort working across the stack — from backend services and databases to CLI tools and web UIs - Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners Preferred qualifications - 5+ years of experience building deployment, release, or delivery infrastructure at scale - Experience with Python and/or Rust in production systems - Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium) - Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity) - Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback - Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts) The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000—$485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we

Minimum qualifications: Strong software engineering skills, including experience designing systems that manage complex state machines and multi-stage pipelines; Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration; Experience building deployment, release, or delivery infrastructure where resource constraints shape the design; A track record of building automation that measurably improves deployment velocity and reliability; Comfort working across the stack — from backend services and databases to CLI tools and web UIs; Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners. Preferred qualifications: 5+ years of experience building deployment, release, or delivery infrastructure at scale; Experience with Python and/or Rust in production systems; Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium); Background in capacity planning or resource-constrained scheduling; Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback; Experience at companies with large-scale release engineering challenges.

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 accelerator chips, minimizing disruption to serving capacity; Evolve self-service model onboarding so new models can be added to the continuous deployment pipeline without Launch Engineering involvement; Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems.