ASIC Architect

Cerebras
Sunnyvale
Workplace: OnsiteFull timeFunction: Hardware, Embedded & Systems EngineeringExperience: 10+ yearsEducation: phdSkills: []

Design ASIC micro-architecture features from high-level architecture specifications for a large-scale AI chip platform. Own performance/power modeling, run PPA trade-offs, and extract efficiency insights. Profile workloads to find bottlenecks and benchmark competitive performance, while collaborating with software teams on end-to-end cluster modeling and identifying kernel-level hardware acceleration opportunities.

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FursaFursa
Cerebras
Cerebras
2 months ago

ASIC Architect

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Source: Company careers pageValidated by: Fursa AI
Last checked: 16 hours agoStatus: Live

Job Summary

Design ASIC micro-architecture features from high-level architecture specifications for a large-scale AI chip platform. Own performance/power modeling, run PPA trade-offs, and extract efficiency insights. Profile workloads to find bottlenecks and benchmark competitive performance, while collaborating with software teams on end-to-end cluster modeling and identifying kernel-level hardware acceleration opportunities.
Location: Sunnyvale
Workplace: Onsite
Employment Type: Full time
Job Function: Hardware, Embedded & Systems Engineering
Seniority: Mid level

Key Responsibilities

  • •Translate high-level architecture specs into micro-architecture feature requirements.
  • •Bring up new features in the performance/power model.
  • •Perform comprehensive power-performance-area (PPA) trade-offs for new architectural features.
  • •Profile workloads to identify bottlenecks and assess competitive benchmarking performance.
  • •Collaborate with software teams on end-to-end application modeling at the cluster level and identify kernel-level hardware acceleration opportunities.

Key Requirements

  • •Masters/PhD in Electrical/Computer Engineering.
  • •10+ years of experience in performance analysis and modeling across GPUs, CPUs, or accelerator products.
  • •Strong background in computer architecture and high-level architectural trade-offs.
  • •Build new performance models from scratch in Python (or similar analytical environments).
  • •Exposure to micro-code (kernel) performance bottlenecks and optimization techniques.
Experience:10+ yearsAI hardwareAccelerators
Education:PhD / Doctorate in Electrical/Computer Engineering
Tech Stack:PythonGPUsCPUsMicro-codePPAPerformance/power modelingWorkload profilingML workload profilingModel network architecture

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
WebsiteLinkedIn