Computer Vision Research Engineer (Internship)

Website Leav

Next AI Tor21

Leav is working towards a vision of building and deploying innovative shopping solutions in physical retail to bridge the gap between online and offline shopping. We do this through innovation in artificial intelligence with unique approaches to solving needs for a highly developed and process oriented market in the middle of a massive and ongoing digital transition.

Our R&D team is responsible for technical innovation and research in artificial intelligence, specifically computer vision and machine learning. We are looking for candidates with expertise in AI, deep learning, computer vision, and data processing to join us. You will work in a small cross-functional team of engineers tackling real-world problems involving Leav’s Artificial Intelligence initiatives as they pertain to our customers and the retail market in general.

Position Summary:

This position is related to computer vision and deep learning with a focus on object detection, object tracking, and action recognition. Knowledge of hardware management and/or data pipeline deployment is a strong plus.

Duties & Responsibilities:

  • Design, develop, document, and test software within the Leav’s R&D team.
  • Develop Artificial Intelligence and Computer Vision algorithms for Leav’s retail applications.
  • Research new developments in the industry within AI and Retail.
  • Clean and analyze data, and provide feedback on operational improvements to data gathering, pre-processing, and testing processes.

Necessary Skills / Attributes:

  • Pursuing masters in Artificial Intelligence, Computer Science, Electrical Engineering, or other related fields.
  • Strong programming skills in Python.
  • Experience with deep learning frameworks (PyTorch, TensorFlow, Keras, etc.).
  • Strong oral and written communication skills.

Nice to have:

  • Familiarity with computer vision packages (OpenCV, etc.).
  • Experience with cloud computing.
  • Experience with data engineering.
  • Experience working with cameras.

To apply for this job please visit www.linkedin.com.