Staff Kernel Optimzation Engineer

Cerebras Cerebras · Semiconductors · Office · Remote · Software

Staff Kernel Optimization Engineer at Cerebras, focusing on developing and optimizing high-performance software for AI and HPC workloads on their custom processor architecture. This role involves implementing and scaling deep learning operations, building parallel algorithms, and contributing to the efficiency of AI model training.

What you'd actually do

  1. Develop design specifications for new machine learning and linear algebra kernels and mapping to the Cerebras WSE System using various parallel programming algorithms.
  2. Develop and debug kernel library of highly optimized low level assembly instruction and C-like domain specific language routines to implement algorithms targeting the Cerebras hardware system.
  3. Develop and debug high-performance kernel routines in low-level assembly and a custom C-like (CSL) language, implementing algorithms optimized for the Cerebras hardware system.
  4. Using mathematical models and analysis to measure the software performance and inform design decisions.
  5. Develop and integrate unit and system testing methodologies to verify correct functionality and performance of kernel libraries.

Skills

Required

  • C++
  • Python
  • Strong debugging skills
  • Understanding of hardware architecture concepts

Nice to have

  • kernel development and/or testing
  • parallel algorithms
  • distributed memory systems
  • programming accelerators such as GPUs and FPGAs
  • Machine Learning neural networks and frameworks such as TensorFlow and PyTorch
  • HPC kernels and their optimization

What the JD emphasized

  • fully leverage our custom, massively parallel processor architecture
  • fully leverage our custom, massively parallel processor architecture

Other signals

  • Develop high-performance software solutions at the intersection of hardware and software
  • implementing, optimizing, and scaling deep learning operations to fully leverage our custom, massively parallel processor architecture
  • building a library of parallel and distributed algorithms that maximize compute utilization and push the boundaries of training efficiency for state-of-the-art AI models