Senior Research Scientist, Machine Learning; BioFM

AI & Data
· Toronto, Ontario
· Full-time
· Senior level

The role is about designing new deep learning architectures and pre-training methods for models that learn from genome and other
-omics data at large scale. A strong candidate has deep machine learning research skill, a solid grasp of genome biology, and the independence to set direction instead of just applying standard tools. The posting mentions both Senior and Staff levels, so scope may vary with experience.

How it fits the AI era

Biological foundation models are a direct example of AI changing how medicines are found, since they can learn patterns in genetic data that manual methods struggle to see. Much of the model building and experimentation is becoming more automated and faster. What stays human is choosing which biological questions matter, judging whether a result is meaningful, and tying model output back to real drug discovery decisions.

How it builds AI experience

The work itself gives hands-on experience building large pre-trained models on RNA and genome data, which is a rare and valuable area. To get ready, someone could train or fine-tune sequence models on public genomics datasets and write up what the models do and do not learn. Working alongside bioinformatics and drug development colleagues would also build skill in turning model results into practical decisions.

Worth showing

• Genomics or
  -omics data modeling
• Published research or open projects
• Linking ML results to biology

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Deep Genomics

Deep Genomics develops biological foundation models and artificial intelligence architectures to discover and design genetic medicines.