ASIC Architect

Responsibilities

• Translate high level architecture spec to micro-architecture feature requirements
• Bring up new features in the performance/power model
• Perform comprehensive PPA trade-offs for new architectural features
• Extract insights for new features and micro-architecture power efficiency
• Profile workloads, identify bottlenecks and project competition performance for benchmarking
• Engage with SW teams for end-end application level modeling at cluster level
• Identify kernel level HW acceleration level opportunities

Requirements

• Masters/PhD in Electrical/Computer Engineering
• 10+ years of experience across performance analysis and modeling across GPUs, CPUs or accelerator products
• Strong background in computer architecture and key high level architectural trade-offs
• Comfortable standing up new performance models from scratch in Python or similar analytical environments
• Exposure to micro-code (kernel) performance bottlenecks and optimization techniques
• Good understanding of how high-level workloads map to underlying micro-architecture is desired
• Understanding of basic ML workload profiling techniques and model network architecture is preferred
• Why Join 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.
• Read our blog: Five Reasons to Join Cerebras in 2026.
• Apply today and become part of the forefront of groundbreaking advancements in AI!
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Additional Information

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.

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