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 will be doing

In this position, tasks that you will be assigned to at DeepScale include:

1. Developing fast implementations of deep learning kernels on emerging accelerator architectures
2. Designing and implementing compiler transformations for our in-house neural network representation
3. Working with model developers to balance performance and accuracy

What you must bring to the table

1. A PHD IN ELECTRICAL ENGINEERING, COMPUTER ENGINEERING, OR COMPUTER SCIENCE. OR, 10+ YEARS OF INDUSTRY EXPERIENCE.
2. A track record of writing really fast code. (This can be for embedded platforms, accelerator platforms, or supercomputers.)
3. Published papers (e.g. in Supercomputing, IPDPS, or PPOPP) and/or open-source code that demonstrate your skills in writing fast code.

Nice to have

1. Original research contributions in the field of programming languages and compilers
2. Original research contributions on the topic of developing faster or more scalable algorithms

What DeepScale brings to the table

1. Career Growth: An opportunity to learn how to build an impactful product while continuing to advance your research career.
2. People: A team of 20+ engineers (7 of whom have PhDs as of this writing) to collaborate with.
3. Get hands-on experience in entrepreneurship and commercialization of Deep Learning technologies.