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C

Network Architect

Cerebras
Sunnyvale, CAOnsite4 days ago
full-timemidgpt-5customopen-source

About the Role

<div class="content-intro"><p><span data-contrast="none">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.&nbsp;</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559685":0,"335559737":240,"335559738":240,"335559739":240,"335559740":279}">&nbsp;</span></p> <p>Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups.&nbsp;<a href="https://openai.com/index/cerebras-partnership/">OpenAI recently announced a multi-year partnership with Cerebras</a>, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.&nbsp;</p> <p>Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.</p></div><h4>About The Role</h4> <p>As a Network Architect on the Cluster Architecture Team, you will work closely with the&nbsp;vendors, internal networking teams and industry peers to develop best-in-class <strong>front-end&nbsp;datacenter and interconnect</strong> architecture of the current and future generations of the&nbsp;Cerebras AI clusters. You will be responsible for developing proof-of-concept of new&nbsp;network designs and features enabling resilient and reliable network for AI workloads. The&nbsp;role will require cross-functional collaboration and interaction with diverse hardware&nbsp;components (e.g., network devices and the Wafer-Scale Engine) as well as software at&nbsp;several layers of the stack, from host-side networking to cluster-level coordination. The role&nbsp;also requires understanding of network monitoring systems and network debugging&nbsp;methodologies.</p> <h4>Responsibilities</h4> <ul> <li>Design and architect front-end network fabrics for AI/ML and HPC systems.</li> <li>Identify and resolve performance and efficiency bottlenecks, ensuring high resource utilization, low latency, and high-throughput communication.</li> <li>Lead cross-functional technical projects spanning multiple teams and integrating diverse software and hardware components to deliver advanced networking technologies.</li> <li>Foster clear and effective communication across teams and stakeholders.</li> <li>Collaborate with vendors and industry partners to shape network hardware and feature roadmaps.</li> <li>Represent Cerebras in industry forums and technical communities.</li> <li>Serve as the central point of contact for network reliability issues.</li> </ul> <h4>Skills &amp; Qualifications</h4> <ul> <li>Ph.D. in Computer Science or Electrical Engineering + 10 years industry experience or&nbsp;Master’s in CS or EE + 15 years industry experience.</li> <li>8+ Years of experience in large scale network designs in <strong>datacenter and cloud&nbsp;environments.</strong></li> <li>Extensive experience debugging networking issues in large distributed systems&nbsp;environment with multiple networking platforms and protocols.</li> <li>Experience of managing and leading multi-phase and multi-team projects.</li> <li>Networking platforms like Juniper, Arista, Cisco, Open box architectures (Sonic, FOBSS).</li> <li>Networking protocols like <strong>VXLAN, EVPN</strong>, RoCE, BGP, DCQCN, PFC, Streaming telemetry.</li> <li>Familiarity with automation languages like Python, or Go.</li> <li>Familiarity with Network visibility and management systems.</li> <li style="font-weight: bold;"><strong>Prior experience in hyperscalers or cloud service providers is strongly preferred.</strong></li> </ul><div class="content-conclusion"><h4><strong>Why Join Cerebras</strong></h4> <p>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&nbsp; point in our business. Members of our team tell us there are five main reasons they joined Cerebras:</p> <ol> <li>Build a breakthrough AI platform beyond the constraints of the GPU.</li> <li>Publish and open source their cutting-edge AI research.</li> <li>Work on one of the fastest AI supercomputers in the world.</li> <li>Enjoy job stability with startup vitality.</li> <li>Our simple, non-corporate work culture that respects individual beliefs.</li> </ol> <p>Read our blog:&nbsp;<a href="https://www.cerebras.net/blog/5-reasons-to-join-cerebras" target="_blank" data-auth="NotApplicable" data-linkindex="0">Five Reasons to Join Cerebras in 2026.</a></p> <h4>Apply today and become part of the forefront of groundbreaking advancements in AI!</h4> <hr> <p><em>Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer.&nbsp;</em><em>We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. </em><em>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.</em></p> <hr> <p><em>This website or its third-party tools process personal data. For more details, click <a href="https://www.cerebras.net/privacy/" target="_blank">here</a> to review our CCPA disclosure notice.</em></p></div>

Required Skills

PythonAgent Orchestration

About Cerebras

Building the largest AI chips in the world for training massive models.

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