Overview
Read the full description before applying.
MUST HAVE: Experience with deep learning in biomedical research, ideally involving multi-omics or imaging data (e.g., genomics, transcriptomics, MRI, CT).
At M31 Biomedical AI, we are redefining how artificial intelligence understands the human body. Our models power universal segmentation and imaging analysis across multiple medical modalities and now, we’re expanding into multi-omics integration, combining imaging with genomic and molecular data to uncover new biological insights.
We’re seeking a full-time AI Scientist to help develop, test, and apply machine learning models that connect imaging with molecular data. You’ll be working with a diverse team of AI researchers, clinicians, and computational biologists to explore how deep learning can bridge the gap between visual and molecular understanding in human health.
This position is ideal for someone passionate about biomedical AI, multi-modal data, and collaborative, high-impact research.
What You’ll Do
- Design and implement deep learning models that integrate medical imaging with other data types (e.g., genomics, clinical, or molecular data)
- Collaborate with research partners to collect, preprocess, and harmonize imaging and omics datasets
- Train and evaluate models to explore relationships between imaging biomarkers and molecular signatures
- Work closely with data scientists and clinicians to ensure scientific and clinical relevance
- Help extend pre-training technology into multi-omics applications
- Document and maintain reproducible workflows using Git, Python, and cloud-based tools
- Contribute to publications, internal reports, and presentations summarizing key findings
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-omics projects
- Gain exposure to large, high-quality datasets spanning medical imaging, genomics, 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 Computer Science, Biomedical Engineering, Computational Biology, or a related field
- Strong programming experience in Python and familiarity with deep learning frameworks (e.g., PyTorch, MONAI, Transformers)
- Background in machine learning applied to biomedical or life science data
- Understanding of at least one of the following domains:
- Medical imaging (MRI, CT, pathology, etc.)
- Genomics or transcriptomics
- Multi-modal data integration or representation learning
- Experience with data management, reproducibility, and collaborative code development
- Excellent problem-solving, communication, and teamwork skills
- Publications in AI, biomedical imaging, or computational biology on top tire conferences or journals in the past two years
Nice-to-Have
- Experience with foundation models or large-scale pretraining
- Familiarity with biological pathway analysis or radiogenomics
- Previous work involving multi-institutional datasets
Application Requirements
- Resume/CV
- Cover letter describing your experience and motivation for working on multi-omics integration
- 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 – integrating multi-omics data to better understand complex diseases and improve therapeutic discovery.
Job Type: Full-time (12-month renewable contract)
Location: Hybrid remote – Toronto, ON (M5S 1A8)
Compensation: CA$65.00 – $90.00/hour, based on experience
Benefits:
- Flexible schedule
- Work-from-home option
- Mentorship and publication opportunities