Staff+ Software Engineer, Node Infra
Seattle, WAAll locationsSeattle, WAbased hybrid policy:San Francisco, CANew York City, NY Hybrid
$320,000–$485,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand. Node Infra owns the full lifecycle of accelerator capacity at Anthropic. We ingest and provision compute from all major cloud providers and from datacenters custom-built for Anthropic, stand up and scale the clusters behind one of the industry's largest AI compute fleets, and build the health, diagnostics and repair automation that keep every GPU, TPU and Trainium node in the fleet usable and ready to power Anthropic's frontier AI research.
What you'll bring
- Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)
- Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform.
- Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium)
- Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems
- Ability to build alignment across senior stakeholders and communicate effectively at all levels
- A field relevant to the role as demonstrated through coursework, training, or professional experience
- Years of experience required will correlate with the internal job level requirements for the position
- 10+ years of software engineering experience, including time as a technical lead setting direction for a team
- Experience managing large scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency
- Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines
- Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons
- Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads.