Description
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 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
- Experience at companies with large-scale release engineering challenges
The annual compensation range for this role is $320,000-$485,000 USD.