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Full Stack LLM Engineer

Canada, Toronto · Job Posted February 17, 2026
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Job Description

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.

Job Responsibility

  • 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

Requirements

  • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field
  • 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

What we offer

  • 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

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