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Added 2 hours ago
SG
Exclusive S&P Global Ratings Early Careers Virtual Networking Event
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BP
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Added 1 day ago
MA
Machine Learning Research Intern
M31 AI
Toronto, ON
- Full-time
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…
Added 1 day ago
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Data Scientist Intern (Jan 2027 12 Months)
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2027 Winter – GRM, AI Business Analyst Intern – Innovation & AI Centre of Excellence (4 Months)
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2027 Winter – GRM, AI Business Analyst Intern – Innovation & AI Centre of Excellence (4 Months)
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2027 Winter – GRM, AI Developer Intern - Innovation & AI Center of Excellence (4 Months)
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2027 Winter – GRM, AI Developer Intern - Innovation & AI Center of Excellence (4 Months)
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2027 Winter – GRM, AI Business Analyst Intern – Innovation & AI Centre of Excellence (4 Months)
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Intern, AI Data Developer (Winter)
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Added 5 days ago
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M31 AI

Machine Learning Research Intern

Toronto, ON, Canada

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

Apply About employer

Applications open in a new tab.

Job details

Pay: Compensation: CA$28-$32/hour, based on experience
Job type: Full-time
Apply by: Apr 3, 2027
Posting language: English
Posted: Oct 5, 2026

Original job description

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

Apply About employer

More about this job

Responsibilities

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.

Skills

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

Benefits

Flexible Schedule
Work-From-Home Option
Mentorship
Publication Opportunities

Visa sponsorship

Not detected in the job text

Keywords

Python
PyTorch
TensorFlow
MONAI
Transformers
Time-to-Event Analysis
Deep Learning
Clinical Electronic Health Records
Laboratory Results
Clinical Notes
Medical Imaging
Histopathology
Radiology
MRI
CT
Foundation Models
Large-Scale Pretraining
Agentic Systems
Multimodal Data
Biomedical AI
Personalized Medicine
Data Preprocessing
Data Harmonization
Git
Cloud-Based Tools
Reproducible Workflows
Literature Search
Clinical Outcomes
Claude Code
Codex

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