Staff 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.

Infrastructure Architecture, Reliability & Observability

• Drive infrastructure architecture decisions around how AI applications and networking components integrate across the platform, weighing trade-offs at a cross-team level.
• Build observability foundations for controller and operator software — metrics, structured events, tracing, and status reporting surfaced through the Kubernetes API and platform dashboards.
• Design systems that degrade gracefully and self-heal, using controller patterns (reconciliation, backoff, status conditions) to reduce manual intervention.
• Debug and resolve complex issues spanning the Kubernetes control plane, networking (CNI, service mesh, kube-proxy/eBPF datapaths), and workload runtime behavior on GPU-backed infrastructure.
• Define standards for safe rollout of controller and platform changes, including versioning, compatibility, and staged deployment.

Team Technical Leadership

• Set technical direction for how the team builds Kubernetes-native software, establishing patterns for controller design, CRD schema evolution, and testing strategy.
• Lead design discussions and code reviews, holding a high bar for Kubernetes API conventions and idiomatic client-go usage.
• Partner with platform engineering, infrastructure, and product teams to translate real developer and operational needs into clean CRDs, APIs, and controller-managed abstractions.
• Define reusable patterns, shared libraries, and scaffolding that let other teams build correctly on the platform without reinventing integration logic.
• Mentor engineers in Kubernetes internals, controller-runtime patterns, and sound operational judgement.

KPIs

• Reliability, scalability, and usability of AI infrastructure–networking software integrations
• Correctness and maintainability of Kubernetes controllers and operators in production
• Reduction in manual operational effort and config drift across supported components
• Adoption of shared patterns/frameworks and effectiveness of observability tooling across teams

Who You are

• At least 8 years of experience in production-level software development.
• Deep hands-on experience building and operating Kubernetes-native software: custom controllers, operators, CRDs, or admission webhooks — using controller-runtime, client-go, or equivalent.
• Strong understanding of Kubernetes internals: the API server, informer/lister patterns, reconciliation loops, and the object model.
• Strong networking fundamentals — CNI, service mesh, kube-proxy/eBPF datapaths, DNS, load balancing — and experience building software that integrates with these systems.
• Proficiency in Go (strongly preferred) or a similar language, with a track record of shipping well-tested, production-quality code at scale.
• Experience with observability practices — metrics, tracing, structured logging — built into software rather than added afterward.
• Comfortable owning components independently end-to-end, from design through operation, while setting direction for adjacent teams.
• Experience with or strong interest in GPU-backed infrastructure and AI workload patterns is a plus.
• Track record of leading technical design at a staff level and mentoring engineers through practical technical guidance.