DeepScale was founded by the deep learning researchers from UC Berkeley who created SqueezeNet. DeepScale is developing perception systems that enable automated vehicles to interpret their environment in real-time using low-cost hardware.

What you must bring to the table

1. A PHD IN ELECTRICAL ENGINEERING, COMPUTER ENGINEERING, OR COMPUTER SCIENCE.
2. A track record of advancing the state-of-the-art in an application of deep learning (ideally a computer vision or imaging application … but if you did speech-recognition or text-analysis, that's pretty good too)
3. Published papers that either (a) are in top peer-reviewed conferences such as CVPR, NIPS, ECCV, ICCV, or ICML … or … (b) a significant (>100) number of citations on one of your deep learning research publications
4. The ability to design, implement, train and test models in one or more of the leading deep learning frameworks like PyTorch or TensorFlow.

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.