Machine Learning Research Intern

M31 AI; Toronto; Hybrid; CAD 39,000 - 44,000; Full time

Benefits offered by this job

Work-from-home option

Mentorship and publications

Job summary

M31 Biomedical AI is seeking a Research Intern to support biomedical AI research using clinical EHR and imaging data. You’ll work with large-scale foundation models alongside AI researchers, clinicians and computational biologists to push personalized medicine forward.

The role emphasizes hands-on experimentation, data preprocessing, and collaboration across domains, with a strong publication and presentation component. Hybrid remote work with Toronto-based location.

Qualifications

• Hands-on experience building and training deep learning architectures (Transformers, CNNs, U-Net) with clinical EHR and imaging data.
• Pursuing or holding an undergraduate to PhD in Engineering, CS, Math, Biomedical Engineering or related field.
• Strong programming in Python and ML frameworks; experience with reproducible code and cloud tools.

Responsibilities

• Review and debug code for training deep learning models and running experiments.
• Conduct literature searches to summarize state-of-the-art architectures.
• Collaborate with researchers to collect, preprocess, and harmonize clinical data and imaging data.
• Work with data scientists and clinicians to ensure clinical relevance.
• Document workflows and maintain reproducible projects using Git and cloud tools.
• Contribute to publications, reports, and presentations with clear visuals.

Skills

Python

PyTorch

TensorFlow

MONAI

Transformers

CNNs

U-Net

Biomedical data

Data management

Communication

Education

Undergraduate to PhD in STEM

Tools

GitHub

MONAI

Job description

Read the full description before applying.

MUST HAVE: Hands-on experience building and training deep learning architectures (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 Do

• Review and debug code for training deep learning models and running experiments
• Conduct literature search to develop detailed in-depth technical summaries of SOTA deep learning architecture
• 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

• Undergraduate degree or currently pursuing a 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 architecture (Transformers, CNNs, U-Net)
• Background in analyzing biomedical or life science data
• Understanding of at least one of the following domains:
• Clinical data (EHR, laboratory results, disease outcomes)
• 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.

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

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

Compensation: CA$28-$32/hour, based on experience

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