Staff + Senior Software Engineer (Inference Deployment)

• 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
• 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

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office

• Strong software engineering skills, including experience designing systems that manage complex state machines and multi-stage pipelines
• A track record of building automation that measurably improves deployment velocity and reliability
• 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
• Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners
• Comfort working across the stack — from backend services and databases to CLI tools and web UIs
• Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)
• 5+ years of experience building deployment, release, or delivery infrastructure at scale
• Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback
• Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)
• Experience with Python and/or Rust in production systems
• Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts)