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.
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 experience, or Master’s/Bachelor’s in CS or EE + 10 years industry experience.
5+ years of experience in server platform architecture, systems performance engineering, or large-scale infrastructure design for AI/ML, HPC, or performance-sensitive distributed systems.
Deep understanding of x86 server architecture: CPU microarchitecture basics, cache hierarchies, NUMA, memory controllers/channels, and memory bandwidth vs latency tradeoffs.
Strong Linux systems knowledge: profiling and performance analysis, scheduling and syscall overheads, memory management behavior, and practical tuning methodology.
Experience reasoning about high-performance IO paths, including NIC behavior at a systems level, RDMA/RoCE concepts, and NVMe performance characteristics.
Proven ability to create capacity and performance models and validate them empirically with a rigorous benchmarking plan.
Experience working directly with vendors/partners to evaluate platforms, drive issue resolution, and influence roadmaps.
Strong cross-functional communication skills and ability to drive technical decisions through clear tradeoff documents and reviews.
Familiarity with application and system software (C, C++, Python).
Why Join Cerebras
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.
Find out more about what it's like to work at Cerebras here!
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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