Technical Product Manager, AI Storage

Accountabilities

• • Own the vision, strategy, roadmap, and feature priorities for AI storage, defining how customers provision, automate, operate, tier, protect, and scale storage across GPU clusters.
  • Translate requirements from GPU cloud operators, NeoClouds, telecommunications providers, sovereign clouds, and enterprise platform teams into clear product direction and actionable requirements.
  • Partner closely with engineering and architecture teams to define product requirements, assess technical trade-offs, and deliver secure, scalable, reliable storage capabilities.
  • Manage and prioritize the product backlog using customer feedback, production deployment insights, design-partner input, and evolving market requirements.
  • Shape capabilities across parallel and distributed file systems, S3-compatible object storage, block storage, snapshots, data protection, automated tiering, and other storage technologies.
  • Define product positioning, packaging, messaging, and competitive differentiation for AI storage solutions.
  • Develop field-facing materials such as technical briefs, competitive battlecards, reference architectures, and other resources that support customer engagement and sales execution.
  • Act as the storage product lead for strategic accounts, helping field teams translate complex technical capabilities into compelling customer solutions.
  • Represent the product at industry events, analyst briefings, and customer advisory boards while building relationships with storage, silicon, and technology ecosystem partners.
  • Drive alignment between storage, GPU infrastructure, Kubernetes, and AI workload requirements to create a unified and declarative product experience.

  Requirements

  • 5+ years of experience in product management, technical product management, or a senior technical position with ownership of a storage product, platform, or large-scale storage environment.
  • Strong technical knowledge of classical and modern storage technologies, including parallel and distributed file systems such as Lustre, GPFS/Spectrum Scale, BeeGFS, WEKA, VAST, or DAOS.
  • Hands-on familiarity with S3-compatible object storage and block storage environments operating at significant scale.
  • Strong understanding of Linux storage, Kubernetes storage concepts such as CSI, dynamic provisioning and storage classes, software-defined storage, storage automation, and hyperscale or service-provider infrastructure.
  • Practical experience with modern GPU cluster storage, including GPUDirect Storage, RDMA data paths, NVMe-oF, high-throughput ingestion pipelines, and hot/warm/cold storage tiering for training datasets and checkpoints.
  • Ability to translate highly technical customer and engineering requirements into clear product strategies, priorities, and roadmaps.
  • Strong product judgment and ability to evaluate technical trade-offs while balancing customer needs, scalability, reliability, security, and business objectives.
  • Excellent communication and collaboration skills, with the ability to engage effectively with engineers, architects, customers, executives, field teams, and technology partners.
  • Ability to operate effectively in a distributed, fast-moving environment characterized by open-source innovation, technical complexity, and continuous change.

  Benefits

  • Competitive compensation package with strong benefits and stock options.
  • Opportunity to work with cutting-edge open-source cloud and AI infrastructure technologies.
  • Collaboration with highly skilled and passionate colleagues working with major enterprise customers.
  • Professional development and training opportunities.
  • Opportunities to attend industry conferences, technical working groups, and ecosystem events.
  • Customized workstation, with macOS or Windows options.
  • High-energy environment that values openness, collaboration, technical excellence, risk-taking, and continuous growth.
  • Opportunity to influence the future of AI infrastructure and work directly with GPU cloud operators, sovereign clouds, and AI-focused enterprises.