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.
About the Role
We are seeking a versatile and experienced engineer to join our SOTA Training Platform team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack.
Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.
- Contribute to the end-to-end bring up of ML models on Cerebras CSX systems.
- Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning.
- Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization.
- Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups.
- Study emerging training and post-training algorithms and map to Cerebras software architecture and hardware.
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
- 5+ years of relevant industry experience (internship/co-op experience included)
- Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
- Strong debugging skills across performance, numerical accuracy, and runtime integration.
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).
- Proficiency in C/C++ programming and experience with low-level optimization.
- Proven experience in compiler development, particularly with LLVM and/or MLIR.
- Strong background in optimization techniques, particularly those involving NP-hard problems.
- Familiarity with large scale ML systems and state of the art algorithms, including model training and reinforcement learning.
- Competitive salary and benefits package.
- Opportunities for professional growth and career advancement.
- A dynamic and innovative work environment.
- The chance to work on cutting-edge technologies and make a significant impact on the future of AI.
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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Skills Required
- Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field
- 5+ years of relevant industry experience (internship/co-op experience included)
- Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling
- Strong debugging skills across performance, numerical accuracy, and runtime integration
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals
- Proficiency in C/C++ programming and experience with low-level optimization
- Proven experience in compiler development, particularly with LLVM and/or MLIR
- Strong background in optimization techniques, particularly those involving NP-hard problems
What We Do
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art. The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2. Join us: https://cerebras.net/careers/
