About the role
At d-Matrix , we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive , and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together , we can help shape the endless possibilities of AI. Job Title: Software Engineering Intern - Kernels Location: Toronto, Canada Program Duration: 12 weeks: June 1st - August 21st or June 22nd - September 11th Project Overview: As a Software Engineering Intern within our Kernels team, you will play a key role in developing high performance kernels essential for accelerating Machine Learning models. Your
responsibilities will span developing reference implementations for accuracy verification, defining unit tests for implemented operators, performance tuning, scalability analysis across varied problem sizes, and packaging/shipping the final implementations. You will also collect performance metrics and identify bottlenecks to improve core functionality. What You Will Do: Implement high performance kernels in low-level languages (Assembly/ISA experience a plus) Develop, test, and tune kernels for machine learning models and performance Create and automate reference implementations and unit tests Analyze scalability and performance, collect metrics, and troubleshoot bottlenecks Package and share implementations with partner teams Required Skills: Ability to implement high performance kernels in low-level languages; Assembly/ISA coding experience is advantageous Proficiency in Python and/or C++ Solid background in Machine Learning model architecture (e.g., LLMs, CNNs) Experience with ML frameworks such as PyTorch and ML packages like Numpy General understanding of computer architecture (CPU, GPU, custom ASICs, etc.) Currently enrolled in a graduate program (Master's or Ph.D) in a relevant discipline Preferred
