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