Technical Research Assistant

Read the full description before applying.MUST HAVE: Experience with deep learning (transformers, CNNs, U-Net), clinical electronic health records (EHR) and/or imaging data.At M31 Biomedical AI, we are redefining how artificial intelligence understands human health and biology. Our models power universal segmentation and imaging analysis across multiple medical modalities to uncover new biological and clinical insights.We’re seeking a full-time Research Intern to support biomedical AI research involving clinical EHR (labs, flowsheets, clinical notes) and imaging data (histopathology and radiology). The role will involve running experiments with large-scale foundation models. You’ll be working with a diverse team of AI researchers, clinicians, and computational biologists to explore how deep learning can advance personalized medicine and healthcare for patients.This position is ideal for someone passionate about biomedical AI, multi-modal data, and collaborative, high-impact research.What You’ll DoTrain deep learning models and run experimentsCollaborate with research partners to collect, preprocess, and harmonize structured and unstructured clinical data, pathology and radiology images.Conduct literature search to identify and summarize SOTA architecture and  Work closely with data scientists and clinicians to ensure scientific and clinical relevanceDiscover, validate and implement new AI tools to improve workflow efficiencyDocument and maintain reproducible workflows using Git, Python, and cloud-based toolsContribute to publications, internal reports, and presentations summarizing key findingsCreate clear, compelling presentations and visualizations that translate highly technical results for both clinical and technical audiencesWhy Join UsBe part of a leading biomedical imaging AI company recognized for its foundational work in universal segmentationCollaborate with top academic and hospital research teams on cutting-edge multi-modal AI projectsGain exposure to large, high-quality datasets spanning medical imaging and clinical dataWork in a mission-driven environment that bridges scientific research and real-world healthcare impactEnjoy flexible work arrangements, mentorship, and opportunities for authorship and recognitionRequired Skills & BackgroundUndergraduate degree or currently pursuing a master’s or PhD (or equivalent experience) in Engineering, Computer Science, Mathematics, Biomedical Engineering, Computational Biology or a related fieldStrong programming experience in Python and ML frameworks (e.g., PyTorch, TensorFlow, MONAI)Strong understanding of deep learning architecture (Transformers, CNNs, U-Net)Background in analyzing biomedical or life science dataUnderstanding of at least one of the following domains:Clinical data (EHR, laboratory results, disease outcomes)Medical imaging (MRI, CT, pathology, etc.)Experience with data management, reproducibility, and collaborative code developmentExcellent problem-solving, communication, and teamwork skillsNice-to-HaveExperience with foundation models or large-scale pretrainingBiomedical domain knowledge (disease pathophysiology, human anatomy, cellular biology)Experience with agentic coding tools (Claude Code, Codex)Previous work involving multi-institutional datasetsPublication record in AI, biomedical imaging, or computational biologyApplication RequirementsResume/CVCover letter describing your experience and motivation for working on patient-centric clinical foundation modelsGitHub portfolio or publications (optional but encouraged)About M31M31 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 to include multi-modal clinical data, in order to advance patient healthcare, understand complex diseases and improve therapeutic discovery.Job Type: Full-time (12-month renewable contract)Location: Hybrid remote – Toronto, ON (M5S 1A8)Compensation: CA$25-28/hour, based on experienceBenefits:Flexible scheduleWork-from-home optionMentorship and publication opportunities