Algorithms, C++, Computer Vision (CV), Deep Learning, Python Programming, PyTorch, TensorFlow
As a Machine Learning Trainee at VIDIZMO, you will be part of our development team, working under the guidance of experienced Machine Learning and Computer Vision Engineers. This role offers a unique opportunity to gain hands-on experience in the application of machine learning and computer vision techniques to enhance our video content management platform and develop intelligent features that cater to our clients' needs such as Detection and Redaction of Different Models, OCR etc.
This Trainee Program is a Full Time Job Opportunity with market competitive salary, benefits, Organizational Growth and more. FRESH candidates with high intentions to build their career in Computer Vision are encouraged to APPLY. Further, Software Engineering candidates who wish to change their career to Machine Learning and Computer Vision, they can also APPLY with full confidence.
At VIDIZMO, we are a cutting-edge technology company specializing in enterprise video content management solutions. Our platform enables businesses to securely manage, stream, and deliver video content efficiently across various industries. As we continue to innovate and grow, we are seeking a talented and enthusiastic Machine Learning Trainee to join our passionate team. We provide Video Content Management systems to Fortune 5000 companies across the globe and are recognized in Gartner's Magic Quadrant. Our developed systems empower our customers to deliver Live as well as On-Demand Video Streaming to their audience, store and share multimedia content as well as perform Video and Audio Analysis using Machine Learning.
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Recognized in Gartner Magic Quadrant and IDC MarketScape, VIDIZMO has more than two decades of experience in providing enterprise video streaming and media management systems, as well as digital evidence management solutions with cutting-edge Artificial Intelligence features for specific use cases. We offer deployment in any public/private cloud in addition to on-premise and hybrid data centers.