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.
Location Options: Sunnyvale, Toronto
About the Role
We are seeking a new college graduate or early career Compiler Engineer to help design and implement new features in our low-level compiler toolchain including the compiler mid-end, backend, assembler, and linker targeting individual cores in this unique architecture. You’ll work primarily within the LLVM infrastructure, developing code generation and optimization strategies for both existing and future architectures.
This role focuses on generating highly optimized single-core code, foundational to scaling performance across our massively parallel system.
Responsibilities
- Design and implement low-level compiler components (compiler backend, assembler, linker) targeting single cores.
- Automate generation of new LLVM targets using high-level architecture description
- Identify and develop novel LLVM mid-end and backend passes that leverage architectural features and optimize code generation for performance, including memory usage, instruction scheduling, and register allocation.
- Analyze performance bottlenecks and iterate on codegen strategies for complex workloads.
- Work closely with hardware architects, kernel developers, and high-level language designers to ensure end-to-end performance.
- Participate in technical reviews, design discussions, and collaborative debugging.
Requirements
- Bachelor’s, Master’s, PhD, or foreign equivalents in computer science, engineering, or related field
- Strong conceptual or hands-on experience with LLVM, particularly in backend code generation.
- One or more years of related work experience on compilers/toolchain development or systems programming
- Strong proficiency in C++, especially modern C++ practices.
- Understanding of computer architecture, instruction sets, and memory models.
- Familiarity with linkers, assemblers, and binary formats.
Preferred
- Exposure to AI/ML workloads and compilers (MLIR, XLA, TVM, etc.).
- Understanding of multi-dimensional data representations and vectorized operations.
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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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
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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/
