Key Skills

Job Description

About Nebul At Nebul, we’re building Europe’s sovereign AI cloud — trusted, secure, and purpose-built for the next generation of intelligent infrastructure. Nebul operates its own technology stack end-to-end. From bare-metal servers and GPU infrastructure to networking, Kubernetes, platform services, and AI workloads, we build and operate the foundations that make AI possible without relying on public cloud hyperscalers. Our challenge is to turn that infrastructure into a reliable, scalable platform that makes GPU capacity and AI capabilities accessible to customers. That requires clear architecture, strong automation, and close collaboration across our engineering teams. As Nebul continues to scale, we’re looking for a Technical Product Owner Reporting directly to the CEO, you’ll translate our platform vision into technical building blocks and coordinated delivery. You’ll have significant ownership and a direct impact on the capabilities we bring to market and Nebul’s success. What You’ll Be Doing As a Technical Product Owner you’ll shape how Nebul’s GPU and AI platform develops and make sure our teams know what to build to get there. You’ll connect the long-term roadmap with the technical work needed today. This means breaking platform ambitions into concrete capabilities, defining the architecture behind them, and translating that architecture into building blocks with clear requirements, interfaces, ownership, and dependencies. Your scope will connect GPU infrastructure, Kubernetes, workload orchestration, storage, networking, and AI platform services. You’ll help define how these layers work together to support the provisioning, deployment, execution, and operation of demanding AI workloads. Working directly with the CEO and closely with our Cloud, Platform, AI, Infrastructure, and Operations teams, you’ll turn strategic priorities into executable plans. You’ll ensure every team understands what it needs to deliver, how its work fits into the platform, and what other teams need from it. You’ll remain deeply technical and hands-on. You’ll develop reference architectures, review designs, validate approaches through technical investigations and proofs of concept, and work through implementation challenges alongside engineers. The ideal candidate can move comfortably from a discussion about the platform’s future to the technical detail of GPU scheduling, Kubernetes architecture, storage performance, or service interfaces. You can see the destination, break it into manageable building blocks, and guide teams through delivery. Key Responsibilities Translate Nebul’s GPU and AI platform roadmap into concrete platform capabilities, technical building blocks, and delivery milestones. Work directly with the CEO to connect business priorities and customer needs with platform architecture and engineering execution. Own and maintain the technical platform roadmap, making scope, priorities, dependencies, and expected outcomes clear. Define how GPU infrastructure, Kubernetes, networking, storage, and AI services fit together as one platform. Break complex initiatives into technical epics and deliverables with clear ownership, interfaces, and acceptance criteria. Ensure teams understand what they need to build, why it matters, and how their components connect to the wider stack. Define architectures and requirements for GPU provisioning, workload scheduling, resource allocation, and workload isolation. Guide platform capabilities that support AI workloads, including training, inference, and model deployment. Shape requirements for platform APIs, self-service capabilities, automation, and infrastructure lifecycle management. Work with engineering teams on multi-tenancy, access control, observability, reliability, and operational readiness. Assess trade-offs across GPU utilisation, workload performance, scalability, cost, and customer experience. Stay hands-on through design reviews, technical investigations, proofs of concept, and implementation discussions. Align delivery across teams and resolve technical dependencies and blockers. Evaluate technologies and vendor proposals against Nebul’s architecture, sovereignty requirements, and operational needs. Follow initiatives through implementation, ensuring individual components come together as usable, reliable platform capabilities. What Your Day Will Not Look Like Maintaining a backlog without understanding the platform or shaping its architecture. Presenting a high-level roadmap and leaving engineers to work out how everything connects. Focusing only on hardware capacity without considering how customers consume GPU resources and AI services. Passing requirements between teams without clarifying technical decisions, interfaces, or ownership. Designing platform services without considering deployment, monitoring, security, upgrades, and day-to-day operations. What You Bring Experience in platform engineering, cloud infrastructure, or technical architecture, with responsibility for designing and delivering complex systems. A strong, current technical foundation and the ability to work hands-on with engineers on architecture and implementation challenges. Experience translating a platform vision or roadmap into technical components, interfaces, dependencies, and achievable delivery plans. Strong practical knowledge of Kubernetes and cloud-native platforms, including workload orchestration, networking, storage, and resource management. Experience with GPU infrastructure or platforms supporting AI/HPC workloads, and an understanding of how these workloads differ from general-purpose applications. An understanding of the infrastructure and platform requirements behind model training, inference, and model serving. Experience designing platform services, APIs, and automation that turn infrastructure into capabilities customers or engineering teams can use. A solid understanding of Linux, distributed systems, networking, storage, and multi-tenant platform design. The ability to reason about performance, reliability, security, scalability, and cost across the full platform stack. Experience aligning multiple engineering teams around shared goals and clearly defined technical deliverables. Confidence challenging designs, evaluating technologies, and explaining architectural trade-offs to engineers and senior leadership. A structured, self-starting approach and the ownership to take initiatives from an ambitious idea through to a working platform capability. Bonus Points If You Have Hands-on experience with NVIDIA GPU infrastructure, GPU operators, device plugins, or GPU resource management in Kubernetes. Experience with AI workload scheduling, distributed training, inference platforms, or model-serving frameworks. Knowledge of GPU sharing and isolation approaches, including MIG, and their implications for utilisation and workload performance. Experience with high-performance storage and networking for AI workloads, including Ethernet, InfiniBand, or RDMA. Experience building self-service platforms, provisioning APIs, usage metering, or quota management. Knowledge of OpenStack, bare-metal provisioning, infrastructure-as-code, and infrastructure automation. Experience operating GPU or AI platforms in production, including observability, capacity planning, upgrades, and incident handling. A background working for a cloud provider, GPU infrastructure provider, or AI platform company. Knowledge of sovereign cloud environments and the technical implications of data residency, security, and compliance requirements. Eligibility & Application Information We welcome non-native Dutch speakers to apply. However, to be eligible, you must: Reside in the Netherlands and be able to travel to our office in Leiden, near The Hague. Be fluent in English; Dutch is not required. Ready to shape the platform behind Europe’s sovereign AI cloud? Apply now through Frank Poll and help turn Nebul’s GPU and AI ambitions into platform capabilities our teams can build and our customers can use.

Core Responsibilities

Translate Nebul’s GPU and AI platform strategy into architectures, technical building blocks, and coordinated delivery plans across infrastructure and engineering teams. Guide platform design and implementation, including GPU provisioning and scheduling, Kubernetes, storage, networking, AI services, APIs, automation, and operational readiness.

Requirements

Candidates should have substantial experience designing and delivering complex platform, cloud infrastructure, or technical architecture systems, with strong practical knowledge of Kubernetes, cloud-native infrastructure, and AI or GPU workloads. They should be able to turn roadmaps into clear technical deliverables, align multiple engineering teams, and assess trade-offs involving performance, reliability, security, scalability, and cost.