Machine Learning Software Engineer – Internship (PEY 2025)

Cerebras Toronto

Cerebras is based in Sunnyvale, California, with its second engineering hub – the AI Centre of Excellence – located in Downtown Toronto. Toronto houses our Machine Learning and Software organization and has some of the most talented ML, optimization, and high-performance computing talent in the world. We have already built out an experienced team of over 100 engineers and computer scientists that are driving forward the next generation of our machine learning stack. 

Responsibilities

• Create tools and design workflows that enable the development, training, and deployment of machine learning models on our new hardware system
• Map abstract computations expressed via third-party ML frameworks into representations that can then be compiled into highly optimized executables that target Cerebras’ system
• Develop connections between representations of existing deep learning frameworks — such as TensorFlow, Caffe/2, MXNet, CNTK — with our customized back-end
• Understand the runtime environments of existing frameworks and our backend, and develop an execution model connecting them together in a way that is seamless to the user

Requirements:

• Enrolled in the University of Toronto’s PEY program with a degree in Computer Science, Computer Engineering, or other related disciplines
• Understanding of state-of-the-art deep learning model architectures and training protocols
• Direct experience with one ML framework internals (like TensorFlow, PyTorch, ONNX, etc) strongly preferred
• Strong Python and C++ development skills

Preferred:

• Good understanding of how to define custom layers and back-propagate through them
• Experience with supervised deep learning models such as RNNs and CNNs
• Experience in vertical such as computer vision, language modeling or speech recognition

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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