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.
About The Role
This role focuses on characterizing, analyzing, and optimizing the performance of state-of-the-art AI models running on Cerebras’ breakthrough hardware. You will work across the hardware and software stack to identify bottlenecks, improve computational efficiency, and help influence the design of Cerebras’ next-generation AI architecture and software systems.
Responsibilities
Bring up and optimize performance on new generations of the Cerebras WSE.
Build performance models (kernel-level, end-to-end) to estimate the performance of state of the art and customer ML models.
Optimize and debug our kernel micro code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras WSE.
Debug and understand runtime performance on the system and cluster.
Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.
Skills & Qualifications
Bachelors / Masters / PhD in Electrical Engineering or Computer Science.
Strong background in computer architecture.Exposure to and understanding of low-level deep learning / LLM math.
Strong analytical and problem-solving mindset.
3+ years of experience in a relevant domain (Computer Architecture, CPU/GPU Performance, Kernel Optimization, HPC).
Experience working on CPU/GPU simulators.
Exposure to performance profiling and debug on any system pipeline.
Comfort with C++ and Python.
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
- Bachelors, Masters, or PhD in Electrical Engineering or Computer Science with strong computer architecture background
- Exposure to and understanding of low-level deep learning / LLM math
- Strong analytical and problem-solving mindset
- 3+ years experience in Computer Architecture, CPU/GPU Performance, Kernel Optimization, or HPC
- Experience working on CPU/GPU simulators
- Exposure to performance profiling and debug on system pipelines
- Comfortable programming in C++ and Python
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.
