Job Description

Overview

Read the full 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

• Be part of a leading biomedical imaging AI company recognized for its foundational work in universal segmentation
• Collaborate with top academic and hospital research teams on cutting-edge multi-omics projects
• Gain exposure to large, high-quality datasets spanning medical imaging, genomics, and clinical data
• Work in a mission-driven environment that bridges scientific research and real-world healthcare impact
• Enjoy flexible work arrangements, mentorship, and opportunities for authorship and recognition

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
• Cover letter describing your experience and motivation for working on multi-omics integration
• GitHub portfolio or publications (optional but encouraged)

About M31

M31 Biomedical AI is a biomedical imaging company developing foundation models for medical image segmentation and analysis. Our technology enables universal understanding of medical images across modalities and institutions. We’re now collaborating with leading research partners to extend this vision beyond imaging – integrating multi-omics data to better understand complex diseases and improve therapeutic discovery.

Job Type: Full-time (12-month renewable contract)

Location: Hybrid remote – Toronto, ON (M5S 1A8)

Compensation: range CA$28.00 - $35.00, based on experience

Benefits:

• Flexible schedule
• Work-from-home option
• Mentorship and publication opportunities

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