About the job
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.
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
Qualifications
Minimum
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
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)