Shape biomedical AI with multimodal LLM architectures and reinforcement learning at M31 Biomedical AI in Toronto, with onsite work three days weekly.
This full-time AI Scientist develops multimodal biomedical foundation models using SOTA LLM architectures, reinforcement learning, and JEPA. You will collaborate with AI researchers, clinicians, and computational biologists. The work explores deep learning for personalized medicine and healthcare.
Key Responsibilities: Define research direction for biomedical multimodal foundation models
Design experiments assessing model performance and meaningful objectives
Build reproducible modular deep learning codebases
Harmonize clinical data, pathology, and radiology images




Communicate findings through publications and technical presentations
Requirements: Master’s or PhD in a related technical field
Strong understanding of transformers, SOTA LLMs, and reinforcement learning
Python and PyTorch, TensorFlow, or MONAI experience
Biomedical or life science data analysis background
Solid publication record and collaborative communication skills
Apply multimodal modeling, reinforcement learning, Python, and biomedical research to advance foundation models at M31 Biomedical AI.

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📌 Biomedical Multimodal AI Scientist (Ontario)
🏢 M31 AI
📍 Ontario