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
Engineers in the CoDesign and NextGen organization work at the interface of software and hardware helping everything from high performance kernel design, next generation ASIC performance modeling and tuning, new system bringup and software tuning, system robustness and simulations.
As an new engineer you'll be expected to learn the Cerebras products and platforms end to end starting at the base layer of programming the Wafer Scale Engine through kernel development, modeling performance for our software products and validating the work through exercising it on both actual hardware and simulations.
Skills & Qualifications
MS/BS in Computer Engineer, Electrical Engineering or Computer Science
Strong background in computer architecture
Strong analytical and problem solving mindset
Exposure to CPU/GPU Architecture and programming models
Experience working on CPU/GPU simulators
Exposure to performance profiling and debug on any system pipeline
Experience with C++ and Python
Exposure to and basic understanding of machine learning is desired
Location
This is a full-time, in-office position based in Sunnyvale, CA. We offer relocation support for candidates relocating to the Bay Area.
Base salary range: $145,000-$155,000 annually, depending on experience, skills, and qualifications, plus equity.
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
- MS/BS in Computer Engineering, Electrical Engineering, or Computer Science
- Strong background in computer architecture
- Strong analytical and problem solving mindset
- Exposure to CPU/GPU architecture and programming models
- Experience working on CPU/GPU simulators
- Exposure to performance profiling and debug on system pipelines
- Experience with C++ and Python
- Exposure to and basic understanding of machine learning
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.
