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d-Matrix

Software Engineering Intern - Kernels

Reposted 9 Days Ago
Hybrid
Toronto, ON, CAN
Internship
Hybrid
Toronto, ON, CAN
Internship
Develop and tune high-performance ML kernels: implement low-level kernels, create reference implementations and unit tests, analyze scalability and performance, collect metrics, troubleshoot bottlenecks, and package implementations for partner teams.
The summary above was generated by AI

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 Qualifications:

  • Previous internship or project experience related to high performance computing or ML kernel development

  • Familiarity with additional ML frameworks (TensorFlow, etc.)

  • Interest in hardware-software co-design

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.

Top Skills

Asic
Assembly
C++
Cnns
Cpu
Gpu
Hardware-Software Co-Design
Isa
Llms
Numpy
Python
PyTorch
TensorFlow

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