[Hiring] Staff Storage Platform Engineer @Radian Arc

Role Description

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.

• Key role in architecting and evolving the storage platform across edge and core deployments.
• Supports the full lifecycle of AI workloads including distributed inference, fine-tuning, and large-scale model training.
• Combines Staff-level architectural ownership, technical direction, and cross-functional influence with hands-on execution.
• Design and optimize the storage architecture underlying distributed inference stacks.
• Design scalable storage systems capable of feeding thousands of GPUs while balancing throughput, latency, resilience, and cost efficiency.
• Operate at L6 / Staff in terms of long-term design, standards, cross-team influence, and platform direction.
• This is a fully remote role, considering relevant candidates in all locations.

Qualifications

• 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.
• 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.
• 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.

Requirements

• Deep expertise in designing and operating storage platforms optimized for GPU-heavy environments and distributed AI workloads.
• 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.

Benefits

• Attractive compensation package reflecting your expertise and experience.
• A great work environment characterized by friendliness, international diversity, flexibility, and a hybrid-friendly approach.
• Opportunity to be part of a fast-growing scale-up with a mission to make a positive impact.
• Exciting career evolution opportunities.

Company Description

Radian Arc is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected category under applicable law.