HPC Storage Engineer - West Coast

Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on.

We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale.

Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers.

You will be part of the Infrastructure organization, specifically within the team managing Runpod's multi-region storage ecosystem. Including network volumes, local NVMe, and S3-compatible object storage. At Runpod, storage is a critical, high-impact resource; it determines cold start velocity, training job data streaming efficiency, and the reliable persistence of model weights and checkpoints.

This senior, hands-on group operates in tight coordination with SRE, networking, and supply chain teams, as well as our global hardware partners. We eliminate the divide between architectural design and operational execution. The engineers who define our systems also write the code, optimize the fabric, manage on-call rotations, and lead capacity planning. We are a remote-first and Slack-native team that prioritizes rapid delivery and empowers engineers to own complete outcomes instead of just closing tickets.

We're hiring a Senior Storage Engineer to contribute to the design, scaling, and reliability of Runpod's storage platform. This is a hands-on engineering role, not just an administration role. You'll be implementing and maintaining distributed storage deployments, writing the code and automation that operates them.

You will help write what Runpod's storage story looks like for the next several years. Helping to decide which distributed storage systems we bet on, how we tier and place data, how we tune the network paths storage depends on, and what we buy and deploy at petabyte scale. You'll have real latitude to innovate, replacing manual operations with automation, designing the metrics and SLOs the fleet is judged by, and leading capacity expansions and migrations end to end. Because storage sits directly under our customers' training, fine-tuning, and inference workloads, improvements you make show up immediately as faster cold starts, faster jobs, and fewer incidents for more than a million developers.

• Own capacity, durability, availability, and performance characteristics of network volumes, local NVMe, and S3-compatible object storage.

• Tune the full I/O path: device and filesystem configuration, caching and read-ahead strategies, replication and erasure coding trade-offs, and client-side mount behavior.

• Diagnose hard performance problems end to end

• Lead capacity expansions, hardware refreshes, migrations, and rebalances without customer-visible disruption.

• Work with Runpod and partner networking teams to design and tune the network paths storage depends on: high-throughput east-west fabric, MTU and jumbo frames, congestion and flow control, multipath, and NIC/offload configuration.

• Understand and optimize RDMA/RoCE and high-speed IB/Ethernet fabrics as they apply to storage traffic.

• Work closely with network engineering on topology decisions, oversubscription ratios, and cross-region data movement.

• Write production code (Go, Python, or similar) for storage control-plane services, provisioning workflows, data movement pipelines, and monitoring

• Build against and extend APIs: our own control plane, S3-compatible interfaces, CSI drivers, Kubernetes APIs, vendor and cloud provider APIs.

• Automate the operations you'd otherwise do by hand. Manual runbooks are a starting point, not a destination.

• Treat infrastructure as code and participate fully in code review, testing, and CI.

• Instrument the storage fleet so its behavior is legible: IOPS, throughput, latency, error and retry rates, capacity utilization, and per-tenant consumption.

• Build dashboards, SLOs, and alerts that catch degradation before customers do.

• Participate in an on-call rotation for storage systems and drive blameless post-incident follow-through.

• 8+ years in infrastructure, storage, or systems engineering, with substantial ownership of production storage at scale.

• Deep, practical experience with at least one distributed storage system — Ceph, MinIO, Lustre, GPFS/Spectrum Scale, MooseFS, WekaFS, VAST, ZFS-based systems, or comparable.

• Strong Linux internals and storage-stack knowledge: block layer, filesystems, NVMe, page cache, I/O schedulers, NFS/SMB, iSCSI/NVMe-oF.

• Building and/or operating S3-compatible object storage services.

• Solid networking fundamentals with specific experience tuning networks for storage workloads.

• Proficiency in writing and shipping production code in Go, Python, Rust, or similar (not just scripting).

• Hands-on experience with observability tooling (Prometheus, Grafana, Datadog, or equivalent) including designing the metrics, not just consuming them.

• A track record of performance analysis and debugging under real production pressure.- Self-starting with general direction. You take a goal like "network volume read latency is hurting cold starts in EU" and come back with a diagnosis, an options analysis, and a plan without needing the work broken down for you.

• Continuous improvement. You leave systems measurably better than you found them. You notice the recurring toil, the alert that fires every Tuesday, the manual step everyone tolerates and you eliminate it.

• Ownership. You follow problems across team boundaries to resolution instead of handing them off at the edge of your component.

• Collaborative and low-ego (but high confidence).

• Storage for AI/ML workloads: checkpointing, dataset streaming, model weight distribution, GPU-adjacent data locality, GPUDirect Storage.

• Kubernetes storage internals: CSI drivers, PV/PVC lifecycle, StatefulSets, local persistent volumes.

• Bare-metal and colocation experience: hardware selection, vendor management, firmware, physical failure domains.

• Multi-tenant environments where isolation, fairness, and QoS are hard requirements.

• Experience in a fast-growing cloud or infrastructure provider.

What You’ll Receive:

• The competitive base pay for this position ranges from ($180,000 - $260,000). 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.

• $1,200 Home Office & Equipment Stipend- We set you up for success from day one with gear and support to create your ideal workspace

Runpod is committed to maintaining a workplace free from discrimination and upholding the principles of equality and respect for all individuals. We believe that diversity in all its forms enhances our team. As an equal opportunity employer, Runpod is committed to creating an inclusive workforce at every level. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, protected veteran status, disability status, or any other characteristic protected by law. We welcome every qualified candidate eligible to work in the United States; however, we are currently unable to sponsor employment visas.

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