AI Scientist

M31 AI; Toronto; Hybrid; CAD 85,000 - 100,000; Full time

Benefits offered by this job

Work-from-home option

Mentorship opportunities

Publication opportunities

Job summary

M31 Biomedical AI is redefining how AI understands health and biology by powering universal segmentation and imaging analysis across modalities. We seek a full-time AI Scientist to develop multimodal biomedical foundation models, integrating SOTA LLMs and reinforcement learning with clinical and biological data.

You will design experiments, build modular codebases, and collaborate with clinicians and researchers to advance personalized medicine and healthcare outcomes.

Qualifications

• Master’s or PhD (or equivalent) in Engineering, Computer Science, Mathematics, Biomedical Engineering, Computational Biology or a related field.
• Strong theoretical understanding of transformers, SOTA LLM architectures and reinforcement learning.
• Strong programming experience in Python and ML frameworks (e.g., PyTorch, TensorFlow, MONAI).
• Background in analyzing biomedical or life science data.
• Understanding of clinical data (EHR, laboratory results, disease outcomes).
• 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.

Responsibilities

• 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.

Skills

Transformers

SOTA LLM architectures

Reinforcement learning

Python

PyTorch

TensorFlow

MONAI

Biomedical data analysis

Communication & teamwork

Publication track record

Education

Master’s or PhD in Engineering, CS, Math, Biomedical Engineering, Computational Biology

Tools

PyTorch

TensorFlow

MONAI

Job description

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
• 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)
• 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

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

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