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About Netradyne Founded in 2015, Netradyne is a technology company that leverages expertise in Artificial Intelligence, Deep Learning, and Edge Computing to bring transformational solutions to the transportation industry. Netradyne’s technology is already deployed in thousands of vehicles; and our customers drive everything from passenger cars to semi-trailers on interstates, suburban roads, rural highways—even off-road. Netradyne is looking for talented engineers to join our Analytics team comprised of graduates from IITs, IISC, Stanford, UIUC, UCSD etc. We build cutting edge AI solutions to enable drivers and fleets realize unsafe driving scenarios in real-time to prevent accidents from happening and reduce fatalities/injuries. Role and Responsibilities You will help design, implement and commercialize driver monitoring and driver assistance algorithms. You will have access to very large datasets and will get to deploy new algorithms/models into thousands of vehicles. As a machine learning software engineer, you may Design and commercialize algorithms characterizing driving behavior.Design, implement and track key metrics; and architect data-driven solutions.Improve machine learning infrastructure for scalable training and inference.Innovate and develop proof of concept solutions showcasing novel capabilities.Requirements: B Tech, M. Tech or PhD in computer science, electrical engineering or a related area.Excellent programming skills – Python(required) and C++ (desired). 10+ yrs with Research or industry experience within computer vision and machine learningAbility to take abstract product concepts and turn them into reality.Ability to innovate on cutting-edge problems without documented solutions.Hands-on experience in handling edge deployments targeting various HW platforms.Desired Skills: Experience with image, video and time-series dataExperience with Camera calibration, multi-view geometry, 3D reconstruction, SLAMExperience with SOTA multi-camera BEV and Occupancy prediction algorithms.Experience with road scene understanding (objects, lanes, intersections, signs etc.)Experience with successfully applying machine learning to solve a real-world problem. Experience with conducting successful statistical experiments.
