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M31 AI

AI Scientist

Toronto, ON, Canada

Compensation: CA$85,000 - $100,000, based on experience · Full-time

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Job details

Pay: Compensation: CA$85,000 - $100,000, based on experience
Job type: Full-time
Apply by: Apr 26, 2027
Posting language: English
Posted: Oct 28, 2026

Original job description

IMMEDIATE HIRE

Overview

Read the full description before applying.

MUST HAVE: In-depth understanding and experience with developing novel multimodal LLM architectures and Reinforcement Learning. 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 AI Scientist to support the development of multimodal biomedical foundation models incorporating SOTA LLM architectures, training frameworks (i.e. Reinforcement Learning, JEPA) and other methodologies. 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

Define research direction to incorporate SOTA multimodal LLM methodologies to foundational models for biomedical applications
Define data input, model outputs, labels and outcomes to develop clinically and biologically meaningful model objectives.
Design experiments to critically assess model performance
Contribute to publications in leading conferences and journals, and open source software that advance the field of biomedical AI
Build, review and maintain reproducible and modular codebases for training and running experiments with deep learning models
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

Master’s or PhD (or equivalent experience) in Engineering, Computer Science, Mathematics, Biomedical Engineering, Computational Biology or a related field.
Strong theoretical understanding of transformers, SOTA LLM architectures and reinforcement learning
Extensive experience developing multimodal LLM architectures
Strong programming experience in Python and ML frameworks (e.g., PyTorch, TensorFlow, MONAI)
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
Strong publication record in leading academic journals and conferences

Nice-to-Have

Biomedical domain knowledge (disease pathophysiology, human anatomy, cellular biology)
Experience with agentic coding tools (Claude Code, Codex)
Previous work involving multi-institutional datasets

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$85,000 - $100,000, based on experience

Benefits:

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

Apply About employer

More about this job

Responsibilities

Define research direction and develop multimodal biomedical foundation models, including clinically meaningful objectives and experiments to evaluate model performance. Build reproducible training workflows, collaborate with researchers and clinicians on clinical and imaging data, and communicate findings through publications, reports, and presentations.

Requirements

A master's degree, PhD, or equivalent experience in a relevant technical or biomedical field is required, along with strong knowledge of transformers, modern LLM architectures, and reinforcement learning. Candidates need extensive multimodal LLM development experience, Python and machine-learning framework proficiency, biomedical or life-science data experience, and a strong publication record.

Skills

Multimodal LLM Architecture Development
Reinforcement Learning
Transformer Architectures
Biomedical AI
Python
PyTorch
TensorFlow
MONAI
Deep Learning
Medical Imaging
Clinical Data Analysis
Data Management
Reproducible Research
Git
Experiment Design
Scientific Communication

Benefits

Flexible Schedule
Work-From-Home Option
Mentorship
Publication Opportunities

Visa sponsorship

Not detected in the job text

Keywords

Biomedical Foundation Models
Multimodal LLMs
Reinforcement Learning
JEPA
Transformers
Deep Learning
Python
PyTorch
TensorFlow
MONAI
Medical Imaging
Universal Segmentation
Clinical Data
Electronic Health Records
Pathology
Radiology
MRI
CT
Laboratory Results
Disease Outcomes
Data Harmonization
Reproducible Workflows
Git
Cloud-Based Tools
Experiment Design
Personalized Medicine
Computational Biology
Agentic Coding Tools
Claude Code
Codex

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