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:

• Contribute to development on an area such as deep neural network design, embedded software engineering, or data pipeline development
• Work with experts in the field of Deep Learning
• Conduct research and publish papers or file patents on the successful completion of your work

IMPORTANT QUALIFICATIONS:

• High familiarity with Python, including NumPy
• Good software engineering practices
• Solid math or machine learning background
• Good interpersonal communication skills

GOOD-TO-HAVE QUALIFICATIONS

• Already established publication record in Deep Learning

WHAT DEEPSCALE BRINGS TO THE TABLE

1. Career Growth: An opportunity to learn how to build an impactful product while continuing to publish papers and advance your research career.
2. Resources: A well-designed GPU computing farm for training DNNs. Unique datasets.
3. People: A team of 20+ engineers (7 of whom have PhDs as of this writing) to collaborate with.
4. Get hands-on experience in entrepreneurship and commercialization of Deep Learning technologies.