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 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.

Senior Data Engineer

DeepScale is hiring a software engineer to lead our data engineering efforts as we transition to the cloud. Data is a critical part of our engineering efforts at DeepScale; not only do we need efficient serving infrastructure to train our models, our proprietary data-sets we collect and annotate give us a competitive advantage in the models and products we can build.

Core Responsibilities

  • Maintain ETL pipelines and create new ones as needed
  • Design and build the next generation of our data ingestion and serving infrastructure for DL training
  • Build integrations with our annotation partners and manage our data annotation pipeline
  • Leverage deep learning to automate intelligent data selection for annotation

Minimum Qualifications

  • B.S. in CS / ECE or related technical field
  • 2+ years professional experience
  • High familiarity with Python, including NumPy
  • Experience with NOSQL and RDBMS data storage
  • Experience with batch data processing and ETL pipelines
  • Experience building data intensive infrastructure in the cloud

Preferred Qualifications

  • Experience with deploying streaming applications (kafka / kinesis etc.) in the cloud
  • Experience with Docker / containerization technologies
  • Experience in architecting and building large-scale batch processing pipelines using Big Data tools such as Map-Reduce, Spark, Cassandra, etc.
  • Experience with building production ML pipelines (esp. Deep Learning)
  • Experience with scheduling engines such as Airflow