Staff Research Engineer

Netradyne is looking for talented engineers to join our Analytics team comprised of graduates from IITs, IISC, Stanford, UIUC, UCSD etc. As a Staff Research Engineer at Netradyne, you will lead the development of 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. You will collaborate with cross-functional teams to build and deploy scalable and reliable AI solutions in a fast-paced environment. Key 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 research 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. Mandatory Skills: B Tech, M. Tech or PhD in computer science, electrical engineering or a related area. In-depth understanding of Machine learning (Deep learning & Classic ML) and computer vision concepts Excellent programming skills Python (required) and C++ (desired). 6.5+ yrs with Research or industry experience within computer vision and machine learning models. Ability 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. Preferred Skills: Experience with time-series data Experience with Camera calibration, multi-view geometry, 3D reconstruction, SLAM. 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.