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.
Join the inference model team dedicated to bring up the state-of-the-art models, numerically validating and accelerating new model ideas on wafer-scale hardware. You will prototype architectural tweaks, build performance-eval pipelines, and turn hard numbers into changes that land in production.
Key Responsibilities- Prototype and benchmark cutting-edge ideas: new attentions, MoE, speculative decoding, and many more innovations as they emerge.
- Develop agent-driven automation that designs experiments, schedules runs, triages regressions, and drafts pull-requests.
- Work closely with compiler, runtime, and silicon teams: unique opportunity to experience the full stack of software/hardware innovation.
- Keep pace with the latest open- and closed-source models; run them first on wafer scale to expose new optimization opportunities.
- 3 + years building high-performance ML or systems software.
- Solid grounding in Transformer math—attention scaling, KV-cache, quantisation—or clear evidence you learn this material rapidly.
- Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
- Strong debugging skills across performance, numerical accuracy, and runtime integration.
- Prior experience in modeling, compilers or crafting benchmarks or performance studies; not just black-box QA tests.
- Strong passion to leverage AI agents or workflow orchestration tools to boost personal productivity.
- Hands-on with flash-attention, Triton kernels, linear-attention, or sparsity research.
- Performance-tuning experience on custom silicon, GPUs, or FPGAs.
- Proficiency in C/C++ programming and experience with low-level optimization.
- Proven experience in compiler development, particularly with LLVM and/or MLIR.
- Publications, repos, or blog posts dissecting model speed-ups.
- Contributions to open-source agent frameworks.
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
- 3+ years building high-performance ML or systems software
- Solid grounding in Transformer math or quick learning capability
- Comfort navigating the full AI toolchain
- Strong debugging skills across performance and accuracy
- Prior experience in modeling, compilers, or performance studies
Cerebras Systems Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cerebras Systems and has not been reviewed or approved by Cerebras Systems.
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Fair & Transparent Compensation — Pay is considered competitive for an AI‑hardware firm, and many employees are described as generally happy with compensation. Sentiment indicates compensation is viewed favorably while acknowledging variation by role and seniority.
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Healthcare Strength — Health coverage is described as top quality with medical, dental, and vision included. Premiums are reportedly fully covered for employees in some plans, increasing perceived value.
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Flexible Benefits — Work‑from‑home flexibility is regarded as strong. Flexible arrangements complement standard offerings like vacation, sick leave, and paid holidays.
Cerebras Systems Insights
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/
