Software Engineer, Inference Deployment

Anthropic•San Francisco, NY

8h•Hybrid

About The Position

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. Our mandate is to make inference deployment boring and unattended. 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. We're building the systems that make inference deployment continuous and unattended. As a Software Engineer on the Launch Engineering team, 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 — your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so 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. If you've built deployment systems at scale and gravitate toward the hardest problems at the intersection of automation and resource management, this team will give you an outsized scope to work on them.

Requirements

5+ years of experience building deployment, release, or delivery infrastructure at scale
Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines
Experience with deployment systems where resource constraints shape the design — whether that's fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows
A track record of building automation that measurably improves deployment velocity and reliability
Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration
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

Nice To Haves

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)
Experience with Python and/or Rust in production systems

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
Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems