Machine Learning Research Internship

Position:

Machine Learning Researcher Internship 

Term:

May to July 2026 (9 weeks) 

Organization:

NeuTech Foundation

Who We Are

NeuTech Foundation is a Canadian Non-Profit Organization dedicated to making technology more accessible and sustainable for communities across Canada. Our work is organized around three core initiatives: OAISus (Optimizing AI for Sustainability), YouSEN (Youth in Sustainable Engineering), and E-Waste management. Together, these initiatives reflect our belief that technology must be built and distributed with equity, efficiency, and environmental responsibility in mind.

OAISus is our research initiative focused on reducing the energy consumption and improving the efficiency of AI systems without compromising performance. The current trajectory of AI development involves scaling up resource consumption, straining energy grids, and increasing carbon emissions. We propose and build alternative approaches to AI development and deployment that prioritize sustainability, particularly for applications in healthcare, education and consumer electronics. We believe the future of AI can be both impactful and environmentally responsible.

What You'll Do

As a Machine Learning Research Intern for the OAISus (Optimizing AI for Sustainability) initiative, you'll be tasked with a focus area that big tech has largely ignored: making ML models efficient and performant simultaneously. The key to being successful in this role will be rigorously identifying and optimizing the ideal model architecture to accomplish a goal while minimizing energy consumption. Day-to-day responsibilities will include:

• Take published machine learning architectures and modify, train, and test them to gauge performance while systematically tracking energy consumption for analysis
• Develop and deploy lightweight models onto edge hardware, including Android/iOS devices and NVIDIA Jetson Orin Nano, by building functional applications and robust model pipelines
• Work extensively with open-source healthcare data, with a strong focus on processing and analyzing medical imaging
• Optimize and fine-tune open-source Large Language Models (LLMs) for specialized healthcare tasks, such as automated and structured medical report generation
• Maintain accurate documentation including experimental training records, model evaluation metrics, and technical notes
• Conduct literature and background research to inform ongoing projects, identifying novel architectures or unique combinations of models to experiment with
• Assist with prototyping end-to-end applications and conducting iterative testing across different stages of edge deployment
• Contribute to internal technical discussions and assist with presenting findings, performance metrics, and model evaluations to the team

Required Skills & Qualifications

• Currently enrolled in 3rd year or higher in Computer Science, Software Engineering, Computer Engineering, Biomedical Engineering, or a closely related discipline
• Hands-on experience working on real machine learning projects, including training your own models from scratch or fine-tuning existing ones
• A solid foundational understanding of computer vision architectures and their practical implementations
• Familiarity with standard software development practices, version control (eg, Git), and maintaining clean, organized code repositories
• Strong attention to detail in tracking experimental setups, evaluating model performance data, and documenting technical workflows
• Demonstrated ability to learn quickly and contribute in a fast-moving, multidisciplinary environment
• Ability to work both independently and collaboratively, and to take ownership of assigned tasks with minimal supervision
• Willingness to learn and take on unfamiliar problems, particularly bridging the gap between software models and hardware deployment

Desired Skills & Qualifications

• High proficiency in Python and standard deep learning frameworks (e.g., PyTorch, TensorFlow)
• Hands-on experience with model optimization and edge deployment frameworks (e.g., TensorFlow Lite, Core ML, ONNX, or TensorRT)
• Familiarity with mobile app development (Android or iOS) to successfully integrate and test deployed models on user-facing devices
• Prior exposure to working with medical data, specifically medical imaging formats (e.g., DICOM files, radiological datasets)
• Experience working with open-source Large Language Models (LLMs) and adapting them for specific text generation or structuring tasks
• Hands-on experience configuring, testing, or deploying software on edge hardware systems like the NVIDIA Jetson Orin Nano
• Comfort presenting data and participating in technical discussions during team meetings
• Experience conducting structured literature searches to support research planning or architectural selection
• Prior co-op, internship, or research experience in a software engineering, scientific, or R&D setting

How to Apply

Please submit a resume (up to 1 page) and a cover letter (up to 1 page). In your cover letter, we'd specifically like you to address your hands-on experience building and deploying machine learning models, and how your background aligns with our mission of developing AI that is efficient, sustainable, and accessible.