Computer Vision Engineer

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Computer Vision Engineer About us: VisionTrack is a multiple award winning IOT, high throughout / big data insurance telematics & video solution. Role: We are looking for developers with experience in computer vision applications to join one of our agile development teams. We try to avoid silos of knowledge so the role will involve working in all areas of the solution but with more emphasis on computer vision applications deployed on our Autonomise cloud platform. We are looking for someone who can fit into this way of working, so knowledge in Agile / DevOps working practices, including SCRUM, Continuous Integration & Continuous Delivery and someone who can follow and promote best practices in these areas is essential. Essential Skills: Proficient in Python programming (2+ years' experience) Previous experience in the development and use of Convolution Neural Networks (CNN's) for machine vision and image analysis applications Previous development and fast prototyping experience in at one of following specializations of computer vision or machine learning:

Object/face/ pedestrian detectin and tracking Activity Recgnition Camera Calibratin 3D stere, SLAM or Depth estimation from video

Proficient in Linux Experience with Docker Experience with large-scale data sets Proficient with open source machine learning platforms such PyTorch or Keras / tensorflow. Ability to develop using rapid prototyping techniques aimed at demonstrating applications quickly in a fast moving environment. Agile / DevOps

Familiarity f SCRUM Familiarity f DevOps Continuous Integration / Continuous Delivery practices. Good written and excellent spoken communication skills as well as attention to detail.

Desired Skills: • MSc or PhD degree in Computer Science, Computer Vision, Machine Learning, Robotics or related technical field. Python experience Experience shipping computer vision solutions or fast prototyping and development Strong grasp of latest cutting edge research in object detection, similarity, transfer & few-shot learning. Experience with using CNNs models in production. Experience using wide range of cameras or camera agnostic solutions. DevOps CI/CD working / best practices. Automation first approach. Git, including GitFlow & Pull Requests / Peer Reviews. Working with Geospatial data. Academic background in a scientific or technical discipline with some formal computer vision / machine learning content. Publication in Computer vision or machine learning topics

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Location:
Gb
Category:
IT

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