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Nscale

Develops and operates AI data centers

Staff Cloud Native Software Engineer

Backend EngineeringPosted on Nov 9
Full-timeIn-person

About the Job

Requirements

  • Demonstrated experience designing and building Kubernetes-native systems, including controllers, operators, custom resource definitions, and production reconciliation logic.
  • A strong track record of writing maintainable, testable, and resilient Go for long-lived distributed or control plane systems.
  • The ability to define technical direction across a domain spanning multiple engineers or teams.
  • Experience driving cross-team architecture and resolving ambiguity in interfaces, ownership, and design.
  • A deep understanding of Kubernetes control plane patterns, distributed systems failure modes, and operational trade-offs.
  • Experience creating engineering leverage through standards, reusable components, technical guidance, and architectural clarity.
  • Strong judgment in balancing short-term delivery with long-term platform health.
  • Strong written and verbal communication skills, with the ability to align multiple teams around a coherent technical direction.
  • A solid grounding in Linux, networking, and infrastructure fundamentals sufficient to debug cross-layer issues.
  • Experience mentoring other engineers and raising the technical quality of work beyond the immediate team.

Responsibilities

  • Own and evolve the technical direction for a defined cloud-native platform domain, such as control plane orchestration, infrastructure lifecycle, platform APIs, or workload management.
  • Design and review Kubernetes-native systems spanning multiple components or teams within the domain.
  • Make architectural decisions that balance delivery needs, operational realities, and long-term maintainability.
  • Resolve ambiguity in architecture, interfaces, and system boundaries for cross-team work.
  • Act as a technical escalation point for the most complex issues within the domain.
  • Drive cross-team design for APIs, controllers, workflows, and operational behavior within the domain.
  • Establish shared patterns and standards for controller and reconciliation design, API and custom resource definition modeling, rollout safety and compatibility, and observability and operational behavior.
  • Identify and correct technical divergence, overlapping abstractions, and avoidable coupling between systems.
  • Create leverage through reference implementations, shared libraries, and common tooling where appropriate.
  • Lead technically critical initiatives spanning two to four teams or a defined platform area.
  • Unblock delivery by clarifying direction, reducing technical ambiguity, and resolving difficult design problems.
  • Contribute hands-on where necessary to validate patterns, de-risk complex implementation work, or accelerate critical delivery.
  • Ensure systems in the domain are designed to be observable, supportable, and safe to run in production.
  • Mentor senior and mid-level engineers in Kubernetes-native design, distributed systems thinking, and production engineering.
  • Raise engineering quality through code review, design review, and strong technical writing.
  • Influence technical outcomes across teams without relying on formal authority.
  • Work closely with platform, infrastructure, and site reliability engineering stakeholders to support broader platform needs.

About the Company

Nscale

Develops and operates AI data centers

Nscale provides end-to-end AI infrastructure by designing, owning, and operating data centers, GPU compute, networking, storage, and managed AI cloud services for enterprises, governments, and AI developers. It offers a full stack from colocated data centers to high-performance GPUs and networking, plus managed cloud services, so customers can run AI workloads efficiently with sovereign European deployments powered by renewables and US campus expansions. Unlike many peers, Nscale combines complete infrastructure ownership with a focus on sovereign, renewable-powered sites and strong capital partnerships to support large-scale, secure AI workloads. Its goal is to become a leading, scalable AI infrastructure provider that enables governments, enterprises, and AI builders to deploy and run AI applications quickly and securely on sovereign data centers with global capacity.

Company size
1,001-5,000
Company stage
N/A
Total funding
$13.2B
Headquarters
London, United Kingdom
Founded
2024

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