Machine Learning Research Intern

Job summary

Build and maintain reproducible deep learning codebases, research and summarize state-of-the-art methods, and collect, preprocess, and harmonize clinical and medical imaging data. Collaborate with researchers and clinicians to develop and validate biomedical AI tools, and communicate findings through publications, reports, presentations, and visualizations.

Job details

Overview Read the full description before applying. MUST HAVE: In-depth understanding and experience with Transformers, Time-to-event analysis, clinical electronic health records (EHR) and/or imaging data. Available to work full-time and on-site 3 days a week. 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 Machine Learning Research Intern to support biomedical AI research involving clinical EHR (labs, flowsheets, clinical notes) and imaging data (histopathology and radiology). The role will involve building large-scale foundation models and agentic systems. 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 Do Build, review and maintain reproducible and modular codebases for training and running experiments with deep learning models Conduct literature search to develop detailed in-depth technical summaries of SOTA architectures, pretraining objectives and other methodologies Collaborate with research partners to collect, preprocess, and harmonize structured and unstructured clinical data, pathology and radiology images. Work closely with data scientists and clinicians to ensure scientific and clinical relevance Discover, validate and implement new AI tools to improve workflow efficiency Document and maintain reproducible workflows using Git, Python, and cloud-based tools Contribute to publications, internal reports, and presentations summarizing key findings Create clear, compelling presentations and visualizations that translate highly technical results for both clinical and technical audiences 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-modal AI projects Gain exposure to large, high-quality datasets spanning medical imaging 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 Completed undergraduate degree, master’s or PhD (or equivalent experience) in Engineering, Computer Science, Mathematics, Biomedical Engineering, Computational Biology or a related field Strong programming experience in Python and ML frameworks (e.g., PyTorch, TensorFlow, MONAI) Strong understanding of deep learning architectures (Transformers) and time-to-event analysis Background in analyzing biomedical or life science data Understanding 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 development Excellent problem-solving, communication, and teamwork skills Nice-to-Have Experience with foundation models or large-scale pretraining Biomedical domain knowledge (disease pathophysiology, human anatomy, cellular biology) Experience with agentic coding tools (Claude Code, Codex) Previous work involving multi-institutional datasets Publication record in AI, biomedical imaging, or computational biology Application Requirements Resume/CV Cover letter describing your experience and motivation for working on patient-centric clinical foundation models 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 to include multi-modal clinical data, in order to advance patient healthcare, understand complex diseases and improve therapeutic discovery. Start Date: ASAP Job Type: Full-time (12-month renewable contract) Location: Hybrid remote – Toronto, ON (M5S 1A8) Compensation: CA$28-$32/hour, based on experience Benefits: Flexible schedule Work-from-home option Mentorship and publication opportunities

What you’ll do

Build and maintain reproducible deep learning codebases, research and summarize state-of-the-art methods, and collect, preprocess, and harmonize clinical and medical imaging data. Collaborate with researchers and clinicians to develop and validate biomedical AI tools, and communicate findings through publications, reports, presentations, and visualizations.

Requirements

Requires an undergraduate, master’s, or doctoral degree (or equivalent experience) in a relevant technical or biomedical field, strong Python and machine learning framework experience, and knowledge of Transformers and time-to-event analysis. Applicants must have experience analyzing biomedical or life science data and knowledge of clinical data or medical imaging, along with collaborative coding, data management, communication, and teamwork skills.

Benefits

• Flexible Schedule
• Work-From-Home Option
• Mentorship
• Publication Opportunities

Listed skills

• Python · Preferred
• data management · Preferred
• Git · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• PyTorch
• TensorFlow
• MONAI
• Transformers
• Time-to-Event Analysis
• Deep Learning
• Clinical Electronic Health Records
• Medical Imaging
• Foundation Models
• Large-Scale Pretraining
• Agentic Systems
• Data Management
• Reproducible Workflows
• Git
• Multimodal Data

Job areas

• Science & Research
• Technology
• Healthcare
• Data & Analytics
• Software