Technical Research Assistant – AI for Medical Imaging

M31 AI; Toronto; Hybrid; CAD 60,000 - 80,000; Full time

Job summary

A leading AI research team in Toronto is seeking a full-time Technical Research Assistant to work on innovative projects in pediatric neuroimaging. Candidates should have a Bachelor's degree in Computer Science or related field and be proficient in Python and machine learning tools. This role offers hands-on experience and potential for publication. Flexible hours and hybrid work options are available.

Benefits

Flexible schedule

Work from home

Qualifications

• Experience with Python and deep learning frameworks.
• Prior experience working with medical or biological datasets is a plus.

Responsibilities

• Preprocess and clean multi-institutional MRI, CT, and angiography datasets.
• Generate segmentation masks in collaboration with radiologists.
• Build end-to-end segmentation pipelines.

Skills

Familiarity with toolkits like MONAI, nnUNet

Proficiency in Python

Strong grasp of machine learning fundamentals

Experience with 2D or 3D imaging data

Experience using Git

Attention to reproducibility

Education

Bachelor's degree in Computer Science, Biomedical Engineering, or related field

Tools

PyTorch

Git

Jupyter

Job description

Overview

Read the entire description before applying. MUST HAVE : Familiarity with toolkits such as MONAI, nnUNet, ITK-SNAP, MedSAM, 3D Slicer

Are you passionate about machine learning and medical imaging? Do you want to apply your technical skills to projects that matter – like improving pediatric brain health? We are hiring a full-time Technical Research Assistant to join our collaborative team working at the cutting edge of artificial intelligence and medical imaging.

This is an ideal opportunity for graduate students, recent grads, or students completing a practicum, thesis, or capstone project, looking to gain hands-on experience in applied machine learning in healthcare. You\'ll work with large, real-world imaging datasets and contribute directly to building and evaluating deep learning models with real clinical impact.

What You\'ll Do

• Preprocess and clean multi-institutional MRI, CT, and angiography datasets (de-identification, normalization, reformatting)
• Generate segmentation masks in collaboration with radiologists and clinical domain experts using tools like MedSAM and ITK-SNAP
• Implement and evaluate classification models using MONAI to predict clinical outcomes (e.g., recurrence risk)
• Build end-to-end segmentation pipelines with nnUNet for identifying key regions of interest
• Conduct model validation (internal, cross-site, and external testing) using standard metrics like AUROC, Dice, NSD, sensitivity / specificity
• Maintain reproducible workflows and clean, well-documented codebases using Git, Jupyter, and UNIX-based tools
• Collaborate closely with AI scientists, clinicians, and imaging experts across a high-performing interdisciplinary team

Why Join Us

• Gain hands-on experience with one of the most promising areas of healthcare AI : pediatric neuroimaging
• Eligible to be counted as practicum or experiential learning credit for graduate or professional programs (check with your program advisor)
• Learn to develop, deploy, and validate AI models in a healthcare research environment
• Receive mentorship from clinicians, AI researchers, and imaging scientists
• Contribute to peer-reviewed publications and high-impact research
• Work with large-scale imaging data from leading hospitals and research centers

Required Skills & Background

• Bachelor\'s degree in Computer Science, Biomedical Engineering, Physics, or related field (Graduate students encouraged to apply)
• Proficiency in Python and deep learning frameworks (especially PyTorch)
• Strong grasp of machine learning fundamentals, particularly in classification and segmentation
• Experience working with 2D or 3D imaging data (MRI, CT, etc.)
• Familiarity with toolkits such as MONAI, nnUNet, ITK-SNAP, MedSAM, 3D Slicer
• Understanding of common model evaluation metrics (e.g., AUROC, Dice, NSD)
• Experience using Git, UNIX systems, and Jupyter notebooks
• Strong analytical thinking, independence, and attention to reproducibility

Nice-to-Have

• Prior experience working with medical or biological datasets
• Experience generating segmentation masks or annotating imaging data
• Familiarity with pediatric or neurological imaging datasets

Application Requirements

• Resume / CV
• Brief cover letter outlining technical experience and interest in the position
• Unofficial transcript
• GitHub portfolio, code samples, or publications (optional but encouraged)

About Us

M31 is a multidisciplinary AI and health research team working with Canada\'s top hospitals and academic centers, working on projects that bridge cutting-edge machine learning with urgent clinical needs.

Job Types : Full-time, Internship / Co-op

Contract length : 12 months

Pay : 35.00-45.00 per hour

Expected hours : 40 per week

Benefits

• Flexible schedule
• Work from home

Work Location : Hybrid remote in Toronto, ON M5S 1A8