JOB POSTING INFORMATION
Position Type:	Professional Experience Year Co-op (PEY Co-op: 12-16 months)
Job Title:	Physical Allocation Software Engineering Intern
Job Location:	Toronto, ON
Job Location Type:	On-Site
Number of Positions:	1
Salary:	Salary Not Available, 40.0 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 interested in solving the hard graph optimization problems involved in allocating the physical chip resources; for instance place and route optimization. You can expect to be contributing to a small agile team working on challenging optimization problems, and be provided with close mentoring and guidance from senior software engineers. As part of our fast-moving startup, you’ll get to contribute to foundational infrastructure development, as well 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. Responsibilities Join our Physical Allocation team and come learn about our software projects. We'll work together to set the code deliverables for your term with us, but in general you can expect to work in C/C++ and python within a loose agile environment (sprints, thorough code review, continuous integration etc.). Some examples of topics: Identify data-flow bottlenecks and devise algorithmic or heuristic place and route solutions Implement algorithms to partition a network efficiently across multiple devices Export new networks to our device and optimize their performance Design and develop scheduling algorithms to optimize network performance Develop tools to profile, measure, and visualize kernel and network performance
Job Requirements:	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 C/C++, python, bash, git Software development experience including data-structures and complex algorithms Working experience or clear understanding of graph theory and discrete optimization Experience with EDA (Electronic Design Automation) algorithms, e.g. place and route Experience with python data science libraries and visualization tools (pandas, numpy, scikit)
Preferred Disciplines:	Computer Engineering Computer Science Electrical Engineering Engineering Science (Aerospace) Engineering Science (Electrical and Computer) 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
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:	FIXED PEY Co-op: 12 months