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Engineering Manager, KUBE Team - Careers @ Cast AI Skip to content Platform Infrastructure Optimization Kubernetes Workload Optimization Standard Cluster Optimization Karpenter Cluster Optimization Database Optimization Infrastructure Observation Kubernetes Cost Monitoring GPU Optimization GPU Sharing GPU Workload Scaling Policies GPU Cost Visibility Cross-cloud GPU Access Custom GPU Edge Location Enterprise AI Coding Token Optimization Enterprise AI Inference Enterprise Agentic Coding Where do you run Kubernetes? AWS GCP Azure Oracle Cloud Application Performance Automation Official APA® Platform How it works Integrations Environments Customers Customers Cybersecurity DevOps E-Commerce Financial Services Industries Gaming & Entertainment Pharmaceutical SaaS All Case Studies Industries Automotive Software & IT AI & ML Setups Transform your cloud-native operations and maximize Kubernetes cost savings Validate Cast AI Get answers Learn about our advanced features Book a Demo Pricing Resources Get Started Documentation Supported Environments Integrations Spot Instance Availability Map Learn Blog Automation Academy Reports Webinars Join the community APA Hero Program Captain Program Slack Community KubeAuto Day Company CAST AI
About Us Newsroom Events Let’s Work Together Careers Partner Program Referral Program Media Brand Assets Contact us Book a demo Sign in Get started Back to Careers Engineering Manager, KUBE Team Bulgaria; Croatia; Estonia; Greece; Hungary; Latvia; Lithuania; Poland; Romania; Slovakia; Slovenia; Ukraine Apply for this role Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn’t work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We’re headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we’re just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine. Why the KUBE team? KUBE is where Cast AI’s optimization stops being a recommendation and becomes real infrastructure. The Optimization Engine decides what to run and when; KUBE makes it happen — provisioning the compute, networking, and storage under every node, across EKS, GKE, AKS, and our own Cast AI Anywhere, and reconciling all of it continuously. The scale is the point. When demand spikes, KUBE brings up 15,000 nodes in a single cluster and gets every pod scheduled in under 45 minutes — and tears a cluster of that size back down in under 15 minutes — across multiple clouds at once, mixing instance types and spot capacity in a single operation. That is provisioning velocity the hyperscalers’ own tooling doesn’t deliver on its own, and KUBE owns the execution of every node in it. Across a fleet of thousands of customer clusters, the platform makes the same high-stakes Kubernetes decisions — add this node, drain that one, is this change safe — thousands of times a day, and this team is accountable for each one landing cleanly. The problems are genuinely hard, and largely undocumented: We reverse-engineered how to attach independent, arbitrarily-typed nodes into managed clusters (EKS, GKE, AKS) that were never designed to allow it — and turned that into a reliable operation on every provider. We built a highly parallelized engine that manages and reconciles the diverse clusters of thousands of customers concurrently. We authored our own Terraform provider and a unified API that abstracts away the deep implementation differences between cloud providers. We own the duality of a Kubernetes node — every node is simultaneously a live cloud machine and a Kubernetes object, and KUBE keeps the two in lockstep through the full lifecycle: bootstrap, drain, delete, hibernate, resume. We are the SMEs for lower-level infrastructure — operating systems, cloud networking, storage, and virtualization — at Cast AI. Each sprint surfaces new hurdles at the intersection of Kubernetes internals and cloud-provider behavior. If you want ownership of a system operating at real scale, with room to shape both the architecture and