Senior Kubernetes Platform Engineer - AI Compute (Hybrid)

Anthropic; Greater London; Hybrid; GBP 325,000 - 485,000; Full time

Job summary

Anthropic is hiring a Senior Kubernetes Platform Engineer in London to own and extend the Kubernetes scheduler for accelerator fleets. You will scale the control plane and design core cluster services that all workloads rely on, working with research, training, and inference teams to align platform capabilities with workload needs.

The role demands deep Kubernetes expertise, distributed systems experience, and strong communication to coordinate with cloud providers and internal stakeholders.

Qualifications

• Significant software engineering experience building and operating production distributed systems.
• Proficiency in at least one systems language (Go, Python, Rust, or C++).
• Deep hands-on Kubernetes experience beyond basic usage, including schedulers, controllers, and large multi-tenant clusters.
• Ability to debug complex issues across the stack from API to node/network root causes.
• Track record designing for reliability, correctness, and clear failure semantics in critical systems.
• Strong written and verbal communication; able to build consensus with stakeholders.

Responsibilities

• Own, operate, and extend the Kubernetes scheduler for Anthropic's accelerator fleets with plugins and policies.
• Scale the Kubernetes control plane to support large clusters and anticipate bottlenecks.
• Design, build, and operate core cluster services such as service discovery used by all workloads.

Skills

Distributed systems engineering

Go

Python

Rust

C++

Kubernetes

Debugging

Reliability design

Communication

Education

Bachelor's degree

Tools

etcd

apiserver

controller-runtime

kube-scheduler

Job description

Anthropic is hiring a Senior Kubernetes Platform Engineer in London to own and extend the Kubernetes scheduler for accelerator fleets. You will scale the control plane and design core cluster services that all workloads rely on, working with research, training, and inference teams to align platform capabilities with workload needs.

The role demands deep Kubernetes expertise, distributed systems experience, and strong communication to coordinate with cloud providers and internal stakeholders.