We are looking for an Engineering Manager, Datacenter Storage Engineering to lead the team responsible for Runpod's distributed storage infrastructure across all regions. This role owns the end-to-end storage stack - from NAND and NVMe devices through filesystems, transport protocols, and cluster-level deployment - ensuring performance, reliability, and scalability for AI workloads.
You will manage engineers designing and operating large-scale SAN and NFS-based systems, including high-performance shared filesystems for training workloads. This role requires deep technical fluency and architectural leadership, combined with strong people management and operational discipline.
Responsibilities- Own Distributed Storage Architecture: Define, evolve, and operate Runpod's global storage platforms, supporting training, inference, checkpointing, and dataset access at scale.
- Build the Storage Engineering Team: Manage and grow a team of storage and systems engineers. Set clear ownership, technical direction, and operational standards across regions.
- High-Performance Shared Filesystems: Design and operate large-scale SAN and NFS deployments, including performance-sensitive shared storage for GPU clusters.=
- Advanced Filesystems & Platforms: Lead deployments and operations of VAST Data and experience with Lustre or similar parallel filesystems used in HPC and AI environments.
- End-to-End Performance Ownership: Drive performance optimization from NAND and NVMe media through controllers, networking, and client access patterns.
- Next-Generation Storage Technologies: Evaluate and deploy cutting-edge capabilities such as NFS over RDMA, GPU Direct Storage (GDS), and low-latency data paths for accelerated workloads.
- Reliability & Scale: Establish best practices for replication, data tiering, data protection, failure recovery, capacity planning, and lifecycle management.
- Automation & Observability: Build automation for provisioning, expansion, upgrades, and monitoring. Ensure deep observability into throughput, latency, and error characteristics.
- Cross-Functional Collaboration: Partner with Datacenter Networking, GPU Platform, SRE, and Product teams to ensure storage systems meet evolving workload and customer needs.
- Vendor & Partner Management: Own technical relationships with storage vendors, hardware partners, and colocation providers; drive roadmap alignment and issue resolution.
Requirements- Engineering Leadership Experience: 3+ years managing storage, systems, or infrastructure engineering teams in production environments.
- Distributed Storage Expertise: 8+ years designing and operating large-scale storage systems, including SAN and NFS architectures at multi-petabyte scale.
- VAST Data Experience: Hands-on experience deploying, operating, or deeply integrating VAST Data in production environments is required.
- Parallel Filesystems: Experience with Lustre or comparable HPC filesystems (e.g., GPFS, BeeGFS) supporting high-concurrency workloads.
- Low-Level Storage Knowledge: Deep understanding of NAND, NVMe, PCIe, storage controllers, and performance characteristics across the stack.
- High-Performance Data Paths: Proven experience with NFS over RDMA, RDMA-capable transports, or similar technologies. Familiarity with GPU Direct Storage strongly preferred.
- Linux Systems Expertise: Strong Linux internals knowledge, including filesystems, I/O scheduling, memory management, and tuning for performance workloads.
- Operational Excellence: Experience running 24/7 storage platforms with strong incident response, change management, and post-mortem discipline.
- Communication & Leadership: Ability to clearly communicate complex technical tradeoffs and lead teams through high-stakes infrastructure decisions.
- Successful completion of a background check.
Preferred Qualifications- Experience supporting AI training pipelines, large-scale model checkpointing, and dataset streaming workloads.
- Familiarity with RDMA fabrics and close collaboration with datacenter networking teams.
- Experience designing storage systems for multi-tenant isolation and secure data access.
- Background in hyperscale, HPC, or AI-focused infrastructure environments.
- Experience building internal storage platforms or abstractions consumed by product teams.
What You'll Receive:- The competitive base pay for this position ranges from $150,000 - $240,000 USD. This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and location
- Meaningful equity in a fast-growing company- everyone on the team receives stock options - your impact drives our growth, and you share in the upside.
- Generous medical, dental & vision plans
- Flexible PTO- take the time you need to recharge
- Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication
- Join a passionate team on the cutting edge of AI infrastructure - where culture, learning, and ownership are at the heart of how we scale.
