About the Role
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Nebius is looking for a deeply technical Technical Product Manager – Storage to join the team.
Responsibilities
•Own and manage the product backlog for storage service teams (block, file, parallel/HPC, and object storage)
•Lead and coordinate cross-company initiatives involving storage, including data durability, performance, capacity, and cost initiatives
•Work closely with engineering and architecture teams to define product requirements at the level of data paths, consistency models, replication and erasure coding, and performance characteristics, and deliver new storage features
•Make informed technical trade-offs on durability, availability, latency, throughput, and cost, and defend them with data
•Partner with product marketing and technical pre-sales/post-sales teams on technical publications, go-to-market activities, customer engagement, acquisition, and retention related to storage
•Ensure the delivery of storage services that meet high standards for durability, availability, performance, scalability, and cost efficiency, including storage for AI/ML training and inference and HPC
Requirements
•Hands-on experience building storage products in the cloud is the single most important requirement: the candidate must have designed, built, or operated cloud storage services (not just used or integrated them), in senior engineering, architecture, or technical leadership roles at hyperscalers, cloud providers, storage vendors, or other advanced technology companies
•Deep understanding of storage systems internals: block, file, and object storage architectures, distributed storage, replication and erasure coding, consistency and durability models, and the read/write data path
•Hands-on familiarity with technologies and interfaces such as NVMe/NVMe-oF, iSCSI, POSIX and parallel file systems (e.g., Lustre, GPFS/Spectrum Scale, BeeGFS), Ceph, S3-compatible object storage, and RDMA-based data paths
•Ability to reason quantitatively about IOPS, throughput, latency (including tail latency), and cost per usable TB, and to read and interpret benchmarks
•Strong technical expertise in at least two of the following areas:
•Block storage and virtualization (volumes, snapshots, replication, NVMe-oF)
•File and parallel file systems for HPC/AI (Lustre, GPFS, BeeGFS, NFS)
•Distributed storage internals (replication, erasure coding, consistency, repair)
•Storage performance engineering and benchmarking
•Object storage at scale (S3-compatible APIs, metadata, multipart, lifecycle)
•Proven track record of delivering complex technical initiatives requiring coordination across multiple teams or stakeholders
•Product management experience is not required, but a strong willingness to learn and grow into the role is essential
Nice to Have
•Technical leadership experience is a strong plus
•Experience with storage for AI/ML training pipelines and large-scale HPC (checkpointing, data loading at scale, high-throughput sequential and random access patterns)
•Experience creating technical documentation, guides, tutorials, or reference architectures for storage products
Conditions
•Competitive compensation
•Career growth and learning opportunities
•Flexibility and ownership
•Collaborative and innovative culture
•Opportunity to work on impactful AI projects
•International environment and talented teams
•Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
