DeepScale sparked the tiny deep neural network revolution with SqueezeNet. Based in Mountain View, CA, the company develops AI perception software for advanced driver-assistance and autonomous driving, with a focus on implementing efficient neural networks on automotive-grade processors.
DeepScale uses deep learning to build accurate and efficient perception systems that enable automated machines to "see". Our software takes input from sensors and produces an environmental model of the real world. Our prior work has produced neural nets that maintain state-of-the-art accuracy but are up to 500x smaller than other nets designed for the same task. We have thought leaders and experienced practitioners in computer vision, AI-powered 3D reconstruction, and deploying small neural nets in embedded applications.
We are currently looking for PhD students who enjoy the freedom to explore solutions and the ability to lead new projects related to Deep Learning that the company will depend on. Projects will be in the areas of object detection, tracking, depth estimation, drivable area and lanes estimation, and 3D object detection. Motion prediction and semantic scene understanding are also of interest.
In this role, you will:
What DeepScale brings to the table