Job Description
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.
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
Qualifications
- 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
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.
Read our blog: Five Reasons to Join Cerebras in 2026.
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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Job Details
- Category
- Avionics
- Employment Type
- Full Time
- Location
- Sunnyvale, CA
- Posted
About Cerebras
Cerebras Systems builds the world's fastest AI computers. Their Wafer Scale Engine is the largest chip ever built, powering a new class of AI supercomputers that accelerate training and inference by orders of magnitude.
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