Mirantis is the Kubernetes-native AI infrastructure company, enabling organizations to build and operate scalable, secure, and sovereign infrastructure for modern AI, machine learning, and data-intensive applications. Backed by Insight and Sapphire.
About the role
Mirantis is looking for a Technical Product Manager to own storage for k0rdent AI, our control plane for GPU infrastructure and distributed AI workloads. In this role, you will define the storage strategy, roadmap, and feature priorities that determine how operators provision, tier, and scale storage across GPU clusters running large-scale training and inference.
What they're looking for
- Translate requirements from NeoClouds, GPU clouds, telcos, sovereign clouds, and enterprise platform teams into clear product direction
- Partner with engineering and architecture to define requirements, evaluate trade-offs, and ship secure, scalable, reliable storage capabilities
- Manage the storage backlog, using feedback from production deployments and design partners to refine roadmap priorities and positioning
- Define positioning, packaging, and competitive differentiation for k0rdent AI storage
- Create field-facing assets, including technical briefs, battlecards, and reference architectures, support strategic accounts as the storage product lead
- Represent Mirantis at events, analyst briefings, and customer advisory boards, engage storage, silicon, and ecosystem partners on reference architecture alignment
More about this role
Mirantis is looking for a Technical Product Manager to own storage for k0rdent AI, our control plane for GPU infrastructure and distributed AI workloads. In this role, you will define the storage strategy, roadmap, and feature priorities that determine how operators provision, tier, and scale storage across GPU clusters running large-scale training and inference.
GPU cloud storage sits at the intersection of high-performance computing and multi-tenant cloud, and the product must hold both worlds together. You will shape how k0rdent AI reasons about parallel file systems (Lustre, GPFS, BeeGFS, WEKA, VAST, DAOS), S3-compatible object stores for training data and checkpoints, automated tiering between hot NVMe and capacity tiers, and GPUDirect Storage paths that bypass the CPU to feed accelerators at line rate. These layers are typically operated as disconnected silos, with storage admins, platform teams, and ML engineers each owning a slice. Your job is to make them a coherent, declarative product.
You will work directly with engineering to shape requirements, with marketing to sharpen positioning, and with field teams to convert technical depth into wins in competitive GPU cloud...
Browse similar: AI jobs · AI startup jobs · Startup jobs · Remote jobs