Cerebras

5 months ago

Compute Server Platform Architect

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Own server-side platform architecture for Cerebras CS3-based AI clusters, ensuring predictable performance, scalability, and reliability. Translate workload behavior into CPU, memory, IO, PCIe, and host-networking requirements, build performance/scaling models, and validate via microbenchmarks and experiments. Define OS/BIOS/firmware/driver baselines, evaluate emerging server technologies, and lead vendor engagements through qualification, acceptance, and production adoption.

Location: United States, Canada

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Own architecture for server roles in Cerebras clusters, including server types, configurations, and lifecycle strategy.
•Define and maintain server formulas (counts/ratios per CS-3 count), capacity planning, and headroom policy.
•Specify detailed platform configurations including CPU strategy, memory topology, PCIe lane budgeting, NIC selection/placement, and NVMe policy.
•Translate software/runtime flows into measurable hardware requirements and set guardrails for software teams.
•Develop and validate performance/scaling models with microbenchmarks and workload-level experiments, identifying bottlenecks and driving cross-stack fixes.

Key Requirements

•PhD in Computer Science or Electrical/Computer Engineering with 8+ years industry experience, or Master’s/Bachelor’s in CS or EE with 10+ years industry experience.
•5+ years in server platform architecture, systems performance engineering, or large-scale infrastructure design for AI/ML, HPC, or performance-sensitive distributed systems.
•Deep knowledge of x86 server architecture (CPU microarchitecture, cache hierarchies, NUMA, memory controllers/channels) and memory bandwidth vs latency tradeoffs.
•Strong Linux systems knowledge including profiling, scheduling/syscall overhead, memory management, and practical tuning.
•Experience reasoning about high-performance IO paths, including NIC behavior, RDMA/RoCE concepts, and NVMe performance characteristics.

Experience:8+ yearsAI/MLHPCPerformance-sensitive distributed systems

Education:PhD / Doctorate in Computer Science or Electrical/Computer Engineering

Skills:Cross-functional communicationTechnical leadershipDriving technical decisionsCapacity and performance modelingVendor collaboration

Tech Stack:X86CPU microarchitectureNUMAMemory controllersCache hierarchiesLinuxCPU SKUAMDIntelARMPCIeNICNVMeCXLRDMARoCESmartNICDPUCC++

Company Brief

Cerebras

Designs and builds wafer-scale AI accelerators and systems for large-scale deep learning workloads, delivering specialized hardware and software to accelerate model training and inference for enterprises and research institutions.

Industry: Hardware Devices

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Headquarters: Sunnyvale, United States

Founded: 2016

Website LinkedIn

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Cerebras

5 months ago

Compute Server Platform Architect

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: United States, Canada

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Own architecture for server roles in Cerebras clusters, including server types, configurations, and lifecycle strategy.
•Define and maintain server formulas (counts/ratios per CS-3 count), capacity planning, and headroom policy.
•Specify detailed platform configurations including CPU strategy, memory topology, PCIe lane budgeting, NIC selection/placement, and NVMe policy.
•Translate software/runtime flows into measurable hardware requirements and set guardrails for software teams.
•Develop and validate performance/scaling models with microbenchmarks and workload-level experiments, identifying bottlenecks and driving cross-stack fixes.

Key Requirements

•PhD in Computer Science or Electrical/Computer Engineering with 8+ years industry experience, or Master’s/Bachelor’s in CS or EE with 10+ years industry experience.
•5+ years in server platform architecture, systems performance engineering, or large-scale infrastructure design for AI/ML, HPC, or performance-sensitive distributed systems.
•Deep knowledge of x86 server architecture (CPU microarchitecture, cache hierarchies, NUMA, memory controllers/channels) and memory bandwidth vs latency tradeoffs.
•Strong Linux systems knowledge including profiling, scheduling/syscall overhead, memory management, and practical tuning.
•Experience reasoning about high-performance IO paths, including NIC behavior, RDMA/RoCE concepts, and NVMe performance characteristics.

Experience:8+ yearsAI/MLHPCPerformance-sensitive distributed systems

Education:PhD / Doctorate in Computer Science or Electrical/Computer Engineering

Skills:Cross-functional communicationTechnical leadershipDriving technical decisionsCapacity and performance modelingVendor collaboration

Tech Stack:X86CPU microarchitectureNUMAMemory controllersCache hierarchiesLinuxCPU SKUAMDIntelARMPCIeNICNVMeCXLRDMARoCESmartNICDPUCC++

Company Brief

Cerebras

Industry: Hardware Devices

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Headquarters: Sunnyvale, United States

Founded: 2016

Website LinkedIn