About UsDeep 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 InAs 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).
Key Responsibilities
Develop the overall ML Platform vision and strategy in partnership with the ML Platform team and key internal and external stakeholders.
Define and maintain the roadmap for core ML Platform problem areas.
Deeply understand workflows and pain points of ML Scientists and Bioinformaticians across the company; translate those insights into well‑scoped problems, clear user stories, and prioritized backlog items.
Work closely with ML Scientists and ML Engineers to co‑design solutions — owning discovery, scoping requirements, and validating prototypes.
Partner with Tech Leads to plan and coordinate engineering work across multiple concurrent projects – including co-facilitating agile ceremonies to ensure smooth execution.
Support release planning, change management, and rollout activities to drive adoption of new ML Platform features.
Establish strong relationships with key stakeholders (ML Science, Target Identification, Engineering, Platform Biology, Platform Chemistry, Drug Discovery and Leadership); communicate team focus, progress, trade‑offs, and risks through regular updates, demos, and written documentation.
Collect feedback on platform usability, performance, and reliability; iterate on features and processes to enhance efficiency, quality, and reproducibility of ML workflows.
Basic Qualifications
5+ years Product Management experience, including ownership of complex, technical internal platforms.
Hands‑on understanding of ML concepts—either through model building (as ML/Data Scientist) or prior Product Management experience building ML tooling.
Proven track record defining and executing on product strategy and roadmaps that drive measurable business value — especially in complex problem spaces.
Excellent communication skills and experience collaborating with a diverse set of stakeholders — including technical teams and senior management.
Experience working with cross-functional teams in an agile environment.
Previous management experience and/or interest in managing a team.
Natural curiosity and a passion for learning.
Preferred Qualifications
Familiarity with GCP, Kubernetes, ML orchestration/tracking tools (Weights & Biases, MLflow, Kubeflow, Ray).
Domain experience in biotech, genomics, or laboratory systems.
PMP certification or similar project management certificate/licensing
What We Offer
A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery.
Highly competitive compensation, including meaningful stock ownership.
Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program.
Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
Maternity and parental leave top-up coverage, as well as new parent paid time off.
Focus on learning and growth for all employees - learning and development budget & lunch and learns.
Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.
Deep Genomics encourages applications from all backgrounds who seek the opportunity to build the world's leading AI-driven genetic medicine company. If you have a disability or special need, accommodation is available on request for candidates taking part in all aspects of the selection process.
