Senior Research Scientist, Machine Learning (BioFM)

present of AI; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

Deep Genomics is seeking a Senior Research Scientist in Machine Learning (BioFM) to design new deep learning architectures and pre-training methods for models that learn from RNA, genome, and other -omics data at scale. The role requires deep ML research skills and a solid grasp of genome biology, with the independence to chart direction beyond applying standard tools.

Candidates will work alongside bioinformatics and drug discovery teams to translate model findings into actionable biology

Qualifications

• Deep machine learning research skills.
• Solid grasp of genome biology.
• Ability to set direction independently.

Responsibilities

• Design new deep learning architectures and pre-training methods for models that learn from genome and other -omics data at large scale.
• Set direction and independently drive research rather than just applying standard tools.
• Build and experiment with large pre-trained models on RNA and genome data.

Skills

Genomics data modeling

Deep learning research

Independent direction setting

Job description

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.