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Senior Full Stack LLM Engineer - Training

United States; Canada, Sunnyvale · 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. 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.

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
  • 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

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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