JOB POSTING INFORMATION
Position Type:	Professional Experience Year Co-op (PEY Co-op: 12-16 months)
Job Title:	Deep Learning Engineering Intern
Job Location:	TORONTO
Job Location Type:	On-Site
Number of Positions:	1
Salary:	Salary Not Available, 0.0 hours per week
Start Date:	05/06/2024
End Date:	04/25/2025
Job Function:	Engineering
Job Description:	Untether AI is a rapidly growing Toronto startup building a next generation hardware AI accelerators for neural net inference. We're designing integrated circuits that will run neural nets orders of magnitude faster and lower power. This class of chips will be the standard platform for running image recognition, speech synthesis, text to speech and many other applications in data centers, mobile phones and self-driving cars within the next 5 years. We are looking for creative problem solvers who are interested in optimizing neural networks for inference. You can expect to be contributing to a small agile team working on challenging problems in deep learning, software engineering, and mathematics, and be provided with close mentoring and guidance from senior engineers. As part of our fast-moving startup, you’ll be contributing to foundational infrastructure and algorithm development, as well as working on new, unsolved problems.  We encourage interdisciplinarity and learning, so if you have experience spanning hardware and software, come talk with us. But don't worry if you think your experience is too narrowly focussed, we've got great projects for people with a wide range of interests and specializations.
Job Requirements:	Join our Neural Networks team and come learn about our software projects. We'll work together to set the deliverables for your term with us, but in general you can expect to work primarily in Python (including TensorFlow and PyTorch), and some C/C++ within a loose agile environment (sprints, thorough code review, continuous integration etc.). Some examples of topics: Quantization: Develop and implement integer approximations for neural network layers from across myriad application domains (e.g. vision, NLP, recommendation engines) Software development: Build out our customer-facing APIs to enable push-button model quantization, compilation, and on-chip inference acceleration Research: Explore and experiment with automated approaches to optimizing nets for inference We don't have strict skill requirements, but of course prior experience in the tools and skills we use is a plus, including: Experience with NumPy, TensorFlow, PyTorch Experience deploying neural networks for inference Software development experience including data-structures and complex algorithms Experience writing production code Experience with low-level programming, such as assembly language or CUDA Strong math background Hardware experience
Preferred Disciplines:	Computer Engineering Computer Science Electrical Engineering Engineering Science (Aerospace) Engineering Science (Electrical and Computer) Engineering Science (Energy Systems) Engineering Science (Infrastructure) Engineering Science (Machine Intelligence) Engineering Science (Math, Stats & Finance) Engineering Science (Nanoengineering) Engineering Science (Physics) Engineering Science (Robotics) Math & Stats
All Co-op programs:	No
Targeted Co-op Programs:	Targeted Programs Professional Experience Year Co-op (12 - 16 months)

APPLICATION INFORMATION
Application Deadline:	Oct 8, 2023 11:59 PM
Application Receipt Procedure:	Online via system
If by eMail, send to:	lily@untether.ai
U of T Job Coordinator:	Marlyn de los Reyes

ORGANIZATION INFORMATION
Organization:	Untether AI
Division:	Main Office
Website:	https://untether.ai/

ADDITIONAL INFORMATION
Length of Workterm:	FLEXIBLE PEY Co-op: 12-16 months (range)