Staff Storage Platform Engineer (AI Storage) - Remote

AboutRadian Arc.We're specialists in outcome-optimized AI infrastructure - deploying, orchestrating and monetizing GPU compute where data, users and demand actually meet: inside telco networks, at the edge, and in core data centers. Not a generic AI platform. Not a consultancy. We’re the bridge between raw silicon and real-world results.

What impact you will have

Mission: Design, build, and operate the AI storage layer powering large-scale GPU infrastructure, enabling datasets, model artifacts, checkpoints, and inference state to be delivered to compute clusters with extremely high throughput and predictable latency.

You will play a key role in architecting and evolving the storage platform across edge and core deployments, supporting the full lifecycle of AI workloads including distributed inference, fine-tuning, and large-scale model training. The role spans multiple storage architectures used across the platform, including hyperconverged storage currently based on StorPool, local NVMe storage for latency-sensitive workloads and edge deployments, and disaggregated AI storage platforms such as VAST Data and Weka.

As the first dedicated storage platform role in the organization, this position combines Staff-level architectural ownership, technical direction, and cross-functional influence with hands-on execution across storage design, deployment, performance engineering, troubleshooting, platform integration, and operational improvement.

A key responsibility of this role is designing and optimizing the storage architecture underlying distributed inference stacks such as NVIDIA Dynamo, llm-d, or similar inference orchestration frameworks. This includes ensuring that storage systems efficiently support inference workloads through optimized dataset access, model artifact distribution, checkpoint handling, and KV-cache persistence. You will design scalable storage systems capable of feeding thousands of GPUs while balancing throughput, latency, resilience, and cost efficiency, and work closely with compute, networking, and platform engineering teams to ensure seamless integration with the platform orchestration layer.

Because this is currently the primary storage platform role in the company, the position is intentionally hybrid: you are expected to operate at L6 / Staff in terms of long-term design, standards, cross-team influence, and platform direction, while also directly executing critical storage work that, in a larger organization, would be distributed across multiple engineers.This is a fully remote role, we will consider relevant candidates in all locations.

What you'll need

Core Experience
* Strong hands-on experience designing and operating distributed storage systems for high-performance compute environments.
* Proven experience designing storage architectures for large-scale AI inference or training platforms, including dataset distribution, checkpointing, and KV-cache storage patterns.
* Deep knowledge of the Linux storage and I/O stack.
* Strong understanding of AI workload data access patterns.
* Experience optimizing storage for GPU-accelerated workloads.
* WEKA Data Platform(an enterprise high-performance storage system for AI and HPC)
* Familiarity with Kubernetes storage integrations such as CSI.
* Experience operating large-scale storage clusters.
* Experience owning both architecture and direct implementation in lean or fast-scaling environments is strongly preferred.

Advanced AI Storage Expertise

The candidate should have deep expertise in designing and operating storage platforms optimized for GPU-heavy environments and distributed AI workloads.

This includes a strong understanding of how training, fine-tuning, and inference systems interact with storage, and how storage architecture affects throughput, latency, concurrency, checkpoint recovery, dataset distribution, and serving performance.

Relevant expertise includes:
* Strong understanding of storage access patterns for distributed inference and training.
* Experience designing storage platforms that support large dataset ingestion and model artifact distribution at scale.
* Practical experience tuning storage architectures for checkpointing, distributed file access, object access, and high-concurrency inference.
* Familiarity with storage patterns for KV-cache persistence and retrieval.
* Experience optimizing data locality and reducing unnecessary network movement between storage and compute.
* Understanding of how storage performance affects large-scale AI frameworks, model-serving systems, and inference orchestration layers.

Systems & Troubleshooting
* Ability to debug complex cross-layer issues spanning:
* Storage hardware,
* Networking,
* Linux kernel and I/O paths,
* Filesystems,
* Object and block storage layers,
* Kubernetes integrations,
* Distributed workload behavior.
* Strong knowledge of storage hardware, NVMe devices, storage fabrics, and high-performance data paths.
* Experience designing storage obs