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.
About The Role
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.
Responsibilities
Vendor Engagement and Evaluation
- 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.
Internal Storage Pathfinding
- 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.
Security and Compliance Alignment
- 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.
Cross-Functional Collaboration
- 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.
Skills And Qualifications
- 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 namespaces, 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 ability to distill complex storage trade-offs into clear recommendations.
Nice to Have
- Experience designing or deploying internal storage stacks for focused or non-traditional use cases.
- Understanding of storage needs in containerized, multi-tenant, or hybrid environments.
- Familiarity with benchmarking tools and methodologies for profiling storage behavior across diverse scenarios.
Why This Role Matters
Storage is a foundational component of our AI/HPC clusters. The right architecture can make the difference between hitting customer goals—or missing them. Whether integrating with cloud providers or crafting custom internal paths, you’ll shape how data flows, scales, and persists across our entire product line.
People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
Read our blog: Five Reasons to Join Cerebras in 2025.
Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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Top Skills
What We Do
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.
The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.
Join us: https://cerebras.net/careers/
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