Staff Cloud Native Software Engineer

Nscale•Seattle, WA

1d•$220,000 - $265,000

About The Position

Nscale is seeking a Staff Cloud Native Software Engineer to build, operate, and improve cloud-native software integrations that connect AI applications and networking components at scale. This role involves working on shared Kubernetes-based platforms, deployment patterns, observability foundations, infrastructure architecture, and operational tooling to ensure internal teams can run services safely and efficiently on GPU-backed infrastructure. The engineer will collaborate with platform engineering, infrastructure, and product teams to meet developer and operational needs. This position is crucial for the reliability, scalability, and usability of Nscale's software integrations. As a Staff engineer, the individual will own significant components, set technical direction, deliver complex work independently, and mentor others to improve operations and engineering quality.

Requirements

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.
Track record of leading technical design at a staff level and mentoring engineers through practical technical guidance.

Nice To Haves

Experience with or strong interest in GPU-backed infrastructure and AI workload patterns is a plus.

Responsibilities

Design, build, and operate Kubernetes-native software, including controllers, operators, custom resources (CRDs), and admission webhooks, to connect AI applications with core networking components on GPU-backed infrastructure.
Extend Kubernetes control-plane capabilities to support AI workload requirements, such as network policy controllers, CNI/service-mesh integrations, and resource/scheduling extensions.
Own significant components end-to-end and set the technical direction for their design, deployment, and operation across the team.
Build reconciliation loops, informers, and client-go–based tooling to maintain consistent infrastructure state between the API server, networking systems, and AI runtime components.
Develop operational tooling and automation to simplify the deployment, running, and support of Kubernetes-native services for internal teams.
Drive infrastructure architecture decisions regarding the integration of AI applications and networking components across the platform, considering cross-team trade-offs.
Build observability foundations for controller and operator software, including metrics, structured events, tracing, and status reporting via the Kubernetes API and platform dashboards.
Design systems that degrade gracefully and self-heal using controller patterns (reconciliation, backoff, status conditions) to minimize 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 the safe rollout of controller and platform changes, including versioning, compatibility, and staged deployment.
Set technical direction for the team's approach to building Kubernetes-native software, establishing patterns for controller design, CRD schema evolution, and testing strategy.
Lead design discussions and code reviews, maintaining high standards for Kubernetes API conventions and idiomatic client-go usage.
Partner with platform engineering, infrastructure, and product teams to translate developer and operational needs into CRDs, APIs, and controller-managed abstractions.
Define reusable patterns, shared libraries, and scaffolding to enable other teams to build correctly on the platform without duplicating integration logic.
Mentor engineers in Kubernetes internals, controller-runtime patterns, and sound operational judgment.