Scientist – Machine Learning

Toronto, Ontario /
Machine Learning /
About Us
Deep Genomics is a Toronto-based startup company that is changing the future of medicine using artificial intelligence. Founded in 2015, Deep Genomics brings together a multidisciplinary team of world-leading experts in machine learning, genomics, chemistry and biology. Together we are on a mission to develop an AI-powered platform for rapid discovery and development of the best genetic medicines, focusing on oligonucleotide drugs for the treatment of patients with rare genetic diseases.

Ideal Candidate
We are seeking a creative and experienced machine learning scientist to decipher how mutations and genetic medicines influence the molecular world of the cell. The successful candidate will develop machine learning approaches for modeling complex RNA- and protein-level outcomes based on massively parallel assays. The ideal candidate has a proven track record of publishing at top machine learning conferences (NIPS, ICML, ICLR) or has applied deep learning to genomics in a top life sciences journal.


    • MSc (Junior Scientist) or PhD (Senior Scientist) trained in machine learning.
    • Extensive practical experience at systematically designing, training, debugging, and evaluating neural networks using modern frameworks such as Tensorflow, PyTorch, or Theano.
    • Excellent scientific writing and presentation skills.
    • Senior Scientists must demonstrate leadership and ability to introduce new machine learning techniques.

What we offer:

    • A chance to develop machine learning techniques that will save human lives.
    • Inspiring, creative, and fast-moving startup work environment in downtown Toronto
    • Competitive compensation package including stock options
Deep Genomics thanks all applicants, however only those selected for an interview will be contacted.

Deep Genomics welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.