Compute Server Platform 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 top 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. 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. About The Role As a Compute / Server Platform Architect on the Cluster Architecture Team, you will own the server-side platform architecture that enables Cerebras CS3-based AI clusters (training and inference) to deliver predictable performance, scalability, and reliability. Our accelerators are network-attached, so the x86 server fleet is a first-class part of the end-to-end system: it runs critical-path runtime functions (for example orchestration, prompt caching, and IO/control services) and must be co-designed with software for token-level latency, throughput, and cost efficiency. You will translate workload behavior into CPU, memory, IO, PCIe, and host-networking requirements, drive platform evaluations with vendors, and provide technical leadership through qualification and production adoption in close partnership with other function leaders and TPMs. Responsibilities Own the architecture for all server roles in Cerebras clusters, including definitions of server types, configurations, and lifecycle strategy. Define and maintain server formulas (counts and ratios per CS-3 count, cluster size, and workload type) including capacity planning and headroom policy. Specify platform configurations: CPU SKU and core strategy, our vendor roadmap (e.g., AMD, Intel, ARM), memory topology (channels, DIMM type, capacity), PCIe topology and lane budgeting, NIC selection/placement, and local NVMe policy where applicable. Translate software and runtime flows into measurable hardware requirements (CPU utilization, memory bandwidth/latency, bursty IO patterns, queueing and concurrency limits) and communicate clear guardrails back to software teams. Develop performance and scaling models; validate with microbenchmarks and workload-level experiments; identify bottlenecks and drive cross-stack fixes. Define the OS, BIOS, firmware, and driver baseline for each server type; there are other teams that follow these recommendations and apply them on our fleet. Stay current on emerging server technologies (CPU generations, new memory technologies, CXL, NVMe evolutions, SmartNIC/DPU capabilities where relevant) and run proof-of-concept evaluations to determine when to adopt. Lead technical vendor engagements (OEM/ODM and component vendors): influence roadmap, request platform knobs, and drive joint debugging on performance or reliability issues. Define qualification and acceptance criteria (performance, stability, operability) and partner with the Infrastructure Hardware TPM to execute qualification plans and land changes cleanly into production. Support bring-up and rare deployment debugging in lab and staging environments; drive root-cause analysis for regressions spanning firmware, drivers, OS, and runtime behavior. Skills and Qualifications PhD. in Computer Science or Electrical/Computer Engineering and + 8 years industry experie