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Cerebras Systems is seeking a Network Systems Architect to define the scale-out and scale-up network architecture for Cerebras platforms, including proprietary interconnects, protocols, and switching. You will translate workload patterns into measurable fabric requirements and collaborate with applications, compiler, runtime, and systems teams.
Your work focuses on low-latency scale-up networks while maintaining breadth across scale-out and customer-facing networks, deciding when standards-based
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
As a Network Systems Architect, you will define the scale out, and particularly scale-up network architecture for current and future Cerebras platforms, including proprietary accelerator interconnects, protocols, and switching. Requirements will not arrive as a finished bandwidth and latency specification. Working with application, compiler, runtime, and systems teams, you will study communication patterns, workload partitioning and placement, data and memory movement, synchronization, locality, and failure behavior, then translate them into measurable fabric requirements.
Your primary focus is low-latency scale-up and system fabrics, with enough breadth across scale-out and customer-facing networks to define clean boundaries. You will decide when standards-based or routable technology is right and when a simpler custom protocol or switching design produces a better system result.
Hands-on here means that architectural judgment is grounded in prior low-level implementation, modeling, bring-up, or debugging. You will write specifications, guide models and prototypes, make technical decisions, and stay engaged through implementation and qualification.
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.