Cloud Native Software Engineer

About Nscale

Nscale is the GPU cloud engineered for AI. We provide cost-effective, high-performance infrastructure for AI start-ups and large enterprise customers. Nscale enables AI-focused companies to achieve superior results by reducing the complexity of AI development. Our GPU cloud bolsters technical capabilities and directly supports strategic business outcomes, including cost management, rapid innovation, and environmental responsibility.

We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As an Nscaler, you’ll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you’ll be contributing to building the technology that powers the future.

About the Role

We're hiring a Staff Cloud Native Software Engineer to build, operate, and improve the cloud-native software integrations that connect AI applications and networking components at scale.

In this software engineering role, you'll work on shared Kubernetes-based platforms, deployment patterns, observability foundations, infrastructure architecture, and operational tooling that help internal teams run services safely and efficiently on GPU-backed infrastructure. You'll partner closely with platform engineering, infrastructure, and product teams to ensure capabilities meet real developer and operational needs.

This role is important to the reliability, scalability, and usability of Nscale's software integrations. As a Staff engineer, you'll take ownership of significant components and set technical direction across teams, deliver complex technical work independently, and raise the quality of operations and engineering through practical improvements, sound technical judgement, and mentoring.

What You’ll Do

Cloud Native Software Engineering

• Design, build, and operate Kubernetes-native software — controllers, operators, custom resources (CRDs), and admission webhooks — that connects AI applications with core networking components on GPU-backed infrastructure.
• Extend Kubernetes control-plane capabilities to support AI workload requirements, including network policy controllers, CNI/service-mesh integrations, and resource/scheduling extensions.
• Own significant components end-to-end and set the technical direction for how they're designed, deployed, and operated across the team.
• Build reconciliation loops, informers, and client-go–based tooling that keep infrastructure state consistent between the API server, networking systems, and AI runtime components.
• Develop operational tooling and automation that make Kubernetes-native services easier for internal teams to deploy, run, and support.