Storage Architect

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

We are looking for a deeply technical and storage-savvy architect to lead our efforts in defining, selecting, and, where needed, designing storage solutions for our AI and HPC cluster deployments. These deployments range from tightly integrated, in-house systems to complex enterprise-grade solutions that must meet demanding performance and security standards.

This role operates at the intersection of performance engineering, vendor evaluation, and architecture design. You’ll engage with multiple storage vendors to assess their offerings, extract the most relevant capabilities (e.g., latency, throughput, compliance), and map them to the evolving needs of our workloads - training, inference, HPC, or hybrid. A key part of the role is understanding the characteristics of various SW workloads in order to derive and refine their storage requirements.

• Act as the technical lead in evaluating third‑party storage solutions, analyzing vendor roadmaps, performance metrics, security/compliance features, and cost models.
• Ensure storage solutions align with workload‑specific requirements, including throughput, inference latency, encryption, and cloud‑related controls.
• Benchmark and characterize storage solutions from multiple angles—bandwidth, latency, IOPS, scaling behavior, and integration friction.

• Drive the development of both lightweight internal storage configurations and more unconventional in‑house storage solutions for targeted use cases, working directly with a small team of SW engineers.
• Maintain deep expertise in low‑level storage hardware—including media types (e.g., NVMe, SCM), device‑level capabilities, and transport‑layer technologies (e.g., NVMe‑oF) —while tracking vendor roadmaps and emerging trends. Identify components that align with performance targets and map them to workload characteristics.

• Collaborate across architecture and platform teams to ensure that storage designs meet security and compliance expectations for hyperscaler and enterprise environments.
• Stay current on evolving customer expectations and align storage choices accordingly.

• Interface with hardware, software, and deployment teams to validate that selected storage solutions integrate cleanly with system architecture and support operational goals.
• Track and document storage variations across cluster generations and customer‑specific deployments.

• 7+ years of experience in storage architecture, preferably in data‑intensive environments such as AI/ML, HPC, or cloud‑scale systems.
• Deep understanding of file systems, including performance and scalability trade‑offs, shared name spaces, and interfaces such as NFS, POSIX, and object‑based ones like S3.
• Strong understanding of modern storage technologies and protocols: NVMe, SCM, RAID, tiering, encryption, and distributed file systems.
• Experience evaluating enterprise and cloud storage solutions against workload‑driven performance and compliance targets.
• Familiarity with system‑level trade‑offs involving IOPS, bandwidth, latency, durability, and cost.
• Excellent communication skills and the…