About DeepScale
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.

Role:

We are looking for a leader for our engineering projects in the area of object detection, semantic segmentation, depth estimation, and scene understanding. You will be leading a team of excellent, highly-motivated deep learning engineers to develop state-of-the-art technology for our automotive-focused perception software. You will provide technical and people leadership, and work with other managers (including Product Managers) to build technology and products.

Responsibilities:

  • Manage between one and three projects.
  • Lead and grow a team of between 3-8 deep learning engineers.
  • Provide technical guidance to engineers.
  • Write annotation guide, curate data, develop models and perform error analysis as needed.
  • Work with VPE to define OKRs.
  • Work with engineering management to define and improve processes that improve productivity.
  • Work with Product Managers to define product features and direction.

Requirements:

  • 4+ years hands-on experience in deep learning projects related to computer vision.
  • 2+ years of experience leading deep learning projects in an industrial setting.
  • 2+ years of experience managing people.
  • Solid understanding of the mathematical foundations of deep learning.
  • Advanced degree in Computer Science, Electrical Engineering, Physics, Mathematics or Statistics.
  • Experience in developing perception solutions for autonomous driving is a plus.
  • Experience deploying deep learning systems on edge devices (not cloud servers) is a plus.