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Deep Learning Engineer

Slyce was launched four years ago with a goal of becoming the world leader in image recognition and visual search.  Over that time we have raised more than $40MM, built out a processing infrastructure and complete service offering, acquired four companies and signed deals with 15 of the leading retailers in the United States including Best Buy, Home Depot, Nordstrom, and Urban Outfitters.  We are a  bright and highly motivated team with ambitious goals to drive cutting edge technology out to the marketplace and have fun while doing it.  We are now looking to improve our core technologies, innovate in the areas of computer vision and deep learning, and tackle complicated problems in a range of new areas. 

Job Description

Reporting to the CTO, the role of Deep Learning Engineer will design and develop algorithms, models, and software for machine learning tasks in a variety of applications.  Successful candidates should have proven experience in these areas and possess a genuine interest in pushing current techniques forward.  This role is an exciting opportunity to join a newly formed team and ultimately contribute to its future growth.

Candidates should possess some or all of the following:

  • MS or PhD degree in computer science, or significant equivalent experience.
  • Strong understanding of deep learning approaches and models including convolutional neural networks (CNNs), recurrent networks, region proposal networks, and more. 
  • Experiencing applying deep learning techniques to image tasks such as classification, detection, localization, segmentation, and more.
  • Practical knowledge of working with deep learning libraries such as Tensorflow or Caffe
  • Solid programming skills in C++ and/or Python. 
  • Knowledge and experience developing for the GPU or working with libraries like CUDA.
  • Background in probability theory and statistics.
  • Solid grounding in statistical machine learning techniques.
  • Must be a fast learner. You will be expected to stay current with what is happening in the field of machine learning.
  • Flexibility and adaptability to work in a growing, dynamic team.


  • Contribute to commercial R&D projects by developing a variety of algorithms and systems in computer vision, image and video analysis.
  • Improve the accuracy of existing machine learning systems.
  • Develop efficient large scale training systems to facilitate the activities conducted by the team.
  • Optimizing machine learning benchmarks to competitive levels of accuracy.
  • Advance the state-of-the-art in the field, including generating patents and publications in journals and conferences.
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