M/L Computer Vision Engineer

7 days ago


Sunnyvale, United States Insight Global Full time

Must Haves:

  • Bachelor’s degree in electrical engineering, computer science or equivalent relevant experience.
  • Experience with machine learning frameworks such as PyTorch, TensorFlow, and model optimization, training and quantization toolkits.
  • 5+ years of experience in software development for complex real-time systems, imaging and CV algorithms or related signal processing fields using C/C++.
  • Solid modern C/C++ programming and refactoring skills and able to understand and debug heavily threaded code.


Plusses:

  • MS or PhD in EE/CS
  • Experience with deep learning architectures such as CNNs, RNNs, or GANs, particularly for deployment on embedded devices.
  • Theoretical knowledge in the field of machine learning, and computer vision or Audio pipeline and algorithms such as capture, render, codecs.
  • Experience programming in SIMD, VLIW, and/or Vector processors and Familiarity with the custom ISA extensions
  • Experience with low-level SW optimization at instruction level, loop optimization, vectorization, data organization and caching
  • Prior experience with ARM or Risc-V CPUs, or Tensilica DSP architectures
  • Familiarity with open source machine learning libraries and frameworks and experience with machine learning pipelines for data processing, model training, and deployment.


Day-to-Day:

  • Implement, optimize and deploy machine learning models and algorithms that solve complex problems related to computer vision, speech, natural language processing, and other areas of artificial intelligence, specifically on resource-constrained devices.
  • Code ML algorithms for CV and Audio application on customized processors and accelerators in C/C++ for performance, latency, and memory.
  • Develop and debug software in a real-time, embedded, multiprocessor, multi-interface environment.
  • Work closely with cross-functional teams, such as HW Architects, FW Engineers, Algorithm and Application Engineers across multiple disciplines (Vision, Audio) to identify opportunities for optimizing machine learning solutions


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