Senior Staff+ Software Engineer responsible for owning, operating, and scaling Anthropic’s large Kubernetes clusters, building core cluster services and custom controllers, and collaborating with research, training, and inference teams to support AI workloads.
Senior Staff+ Software Engineer, Kubernetes Platform en Anthropic
Híbrido - San Francisco, CA | New York City, NY | Seattle, WA
Más vacantes en AnthropicSalary
USD 405,000 - 485,000
Requirements
Skills
- Significant software engineering experience building and operating production distributed systems
- Proficiency in at least one systems-appropriate language (e.g., Go, Python, Rust, or C++)
- Deep, hands‑on Kubernetes experience (well beyond "user of") into scheduler, controllers, apiserver, or operating large multi‑tenant clusters
- Demonstrated ability to debug complex issues across the stack, from API behavior down to node and network‑level root causes
- A track record of designing for reliability, correctness, and clear failure semantics in systems other engineers depend on
- Strong written and verbal communication; comfort building consensus with internal stakeholders
- Experience with Kubernetes internals or contributions: kube‑scheduler / scheduling framework, apiserver, etcd, client‑go, controller‑runtime, or similar
- Experience building or operating cluster schedulers or batch systems (e.g., Kueue, Volcano, Slurm, or in‑house equivalents)
- Background scaling control planes or coordination systems (etcd, ZooKeeper, Consul, or large DNS/service‑mesh deployments)
- Familiarity with ML infrastructure: GPUs, TPUs, or Trainium; gang scheduling; topology‑aware placement; collective networking such as NCCL
- Experience with GCP and/or AWS, including GKE/EKS internals and Infrastructure as Code
- Low‑level systems experience such as Linux kernel tuning, cgroups, or eBPF
- 12+ years of relevant industry experience, including time leading large, ambiguous infrastructure projects
Responsibilities
- Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets, including custom scheduling plugins and policies for gang scheduling, topology awareness, and preemption
- Scale the Kubernetes control plane (apiserver, etcd, controller‑manager) to support clusters far beyond typical limits, and find the next bottleneck before it finds us
- Design, build, and operate core cluster services such as service discovery that every workload in the fleet depends on
- Build and maintain custom controllers, operators, and CRDs
- Partner with research, training, and inference to understand workload shapes and turn their requirements into platform capabilities
- Collaborate with cloud providers on required features and escalations
- Participate in on‑call, lead incident response, and design processes (postmortems, runbooks, SLOs) that help the team avoid repeating failures
Technologies
GoPythonRustC++Kuberneteskube-schedulercontroller-runtimeclient-goetcdcontroller-managerGKEEKSInfrastructure as CodeLinux kernel tuningcgroupseBPFNCCL
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