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.
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.
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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Skills Required
- Masters or PhD in Electrical or Computer Engineering
- 10+ years experience in performance analysis and modeling across GPUs, CPUs, or accelerator products
- Strong background in computer architecture and high-level architectural trade-offs
- Ability to stand 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
- Understanding of ML workload profiling techniques and model network architecture
What We Do
Cerebras Systems develops wafer-scale semiconductor hardware, AI supercomputers, and software/cloud services for training and inference. Its CS-2 and CS-3 systems help organizations build on-premise AI supercomputers, while pay-as-you-go cloud offerings provide developers and enterprises access to its computing platform. The company focuses on making AI training and inference faster and easier for diverse research and production workloads at scale worldwide.
