About Us
Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of genome biology to identify novel drug targets, mechanisms, and genetic medicinestherapeutics inaccessible through traditional methods. We co-develop drug programs and AI models with partners and internally, and pursue major technology builds with pharmaceutical partners. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team located in Toronto, Cambridge, MA, and select other sites is revolutionizing how new medicines are created.
Where You Fit In
As a Senior Product Manager on the ML Platform team, you will define and drive the vision, strategy, and roadmap for the machine learning infrastructure that powers Deep Genomics’ drug discovery engine. Your work will support both the development and application of machine learning models across a wide range of therapeutic research initiatives.
You’ll work closely with two key customer groups:
Model Builders: ML Scientists, Bioinformaticians, and Data Scientists who develop and iterate on internal and state-of-the-art external models. They depend on the ML Platform for robust tools to train, evaluate, deploy, and share models — with a strong emphasis on reproducibility, scalability, and experimentation speed.
Model Users: Team members applying ML models to accelerate their work in areas such as Target Identification, Molecule Design, and Molecule Optimization. This includes both computational users (e.g., ML Scientists, Bioinformaticians) who work directly with models, and non-computational users (e.g., Target Curation Scientists, Experimental Biologists, Leadership) who need accessible tools to explore and act on model predictions.
You’ll partner closely with teams across Engineering, Product Management, Machine Learning, Target Identification, Platform Biology, Platform Chemistry, and Drug Discovery to ensure the ML Platform meets diverse needs — from enabling cutting-edge model development to scaling model application across research teams and future pharma partners. This hybrid role is based in Toronto, and candidates must be located in or able to relocate to Toronto or the Greater Toronto Area (GTA).
