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 Strong publication record and collaborative communication skills Apply multimodal modeling, reinforcement learning, Python, and biomedical research to advance foundation models at M31 Biomedical AI. #J-18808-Ljbffr

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