Staff Software Engineer, Machine Learning Performance

- Masters or Ph.D. in Computer Science, Electrical, Mechanical, Aerospace Engineering, or a related discipline. - Profound understanding of deep learning principles, heterogeneous computing, ML accelerators, and compilers with over 7 years of relevant industry experience. - Proven team collaborator with exceptional communication skills to facilitate cross-functional initiatives in a dynamic development atmosphere. - Research and development experience in: - Model compression and neural architecture search techniques including knowledge distillation, pruning, quantization, and quantization-aware training; optimizing and deploying inference on diverse embedded processors. - Expertise in defining compute architectures for efficient deep learning inferencing. - Ability to comprehend hardware specifications and utilize performance profiling tools. - Strong proficiency in Python programming and in-depth knowledge of at least one framework such as PyTorch, TensorFlow, or MXNet. - Experience implementing inference logic from foundational principles using low-level routines like BLAS, CUDA Kernels, or native C++. - Familiarity with ONNX runtimes and deep learning compilers like XLA or Glow.