FY25 Intern - Machine Learning Systems Engineering Internship - Canada (16 months)

Before there were smartphones or smart cities, before autonomous cars or 360° virtual reality videos, there was our technology. Headquartered in San Diego, for over 30 years Qualcomm inventions have inspired others to make the impossible, possible. From 5G to artificial intelligence, from IoT to automotive and extended reality applications, Qualcomm is inventing the technologies of an intelligently connected future, spearheading research efforts for the next global wireless standard, and collaborating with industry leaders in the wireless value chain to make this future a commercial reality.

As an Intern on the Qualcomm Machine Learning Systems Team, you will work closely with machine learning software and hardware performance architecture teams working to create and improve ML compilers for Qualcomm chips. The team also creates, deploys, and uses performance-driven simulation components for analysis and design of ML FW, SW and HW. This opportunity will utilize and strengthen the candidates' SW and HW knowledge skill set.

KEY RESPONSIBILITIES:

1. Compile neural networks to Qualcomm hardware, analyze results and prototype algorithms to improve compilers and/or HW.
2. Create simulation models using Python, C, C++ to analyze the performance of neural networks on Qualcomm hardware.
3. Run, debug, and analyze performance simulations and suggest enhancements to Qualcomm software and hardware to tackle bottlenecks due to computing and system memory hierarchy.
4. Improve Qualcomm's compiler stack so that ML/AI models run faster and more efficiently on our ML/AI accelerator.

Programming Languages: · C/C++ · Python

Minimum Qualification: · Python or C++ · Detail-oriented with strong analytical and debugging skills

Preferred Qualifications: · Effective communication skills (written and verbal) · Strong knowledge of Python and/or C/C++ is a strong asset · Strong object-oriented design principles are an asset · Knowledge of software processes (revision control, continuous integration, etc.) · Knowledge in one or more of the following disciplines is preferred: machine learning APIs, convolutional neural networks, large language models, AI accelerator architecture · Working knowledge of digital circuits and event-driven simulators is an asset

Educational Requirements: · In study towards a Bachelors in the following: Electrical or Computer Engineering, Computer Science, Mathematics, Engineering Science, Engineering Physics, System Design Engineering, or a related multi-disciplinary field · Must be available for at least 12 months. 16 months is strongly preferred. · A transcript is required. Please make sure you upload one.