Staff + Senior Software Engineer, Inference Deployment

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.

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.

• Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets.
• Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets.
• Extend deployment observability — dashboards and tooling that answer deployment questions.
• Drive down cycle time from code merge to production.
• Optimize fleet rollout strategies for large-scale deployments.
• Evolve self-service model onboarding.
• Partner with teams across the Inference organization.

• Strong software engineering skills, including experience designing systems that manage complex state machines and multi-stage pipelines.
• Proficiency with Kubernetes-based deployments.
• Experience building deployment infrastructure where resource constraints shape the design.
• A track record of building automation that improves deployment velocity.
• Comfort working across the stack.
• Strong communication skills.

• 5+ years of experience building deployment infrastructure at scale.
• Experience with Python and/or Rust in production systems.
• Experience with ML inference or training infrastructure deployment.
• 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.

Minimum education: Bachelor’s degree or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time.