Top AI & Machine Learning Jobs in Toronto, ON

Graduate research co-op focused on building Customer World Models and proactive intelligence: designing representation learning, foundation-model, reinforcement learning, and agentic systems; running experiments, publishing, and deploying research into production to improve long-horizon decision-making and customer-facing automation.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Lead design and delivery of production autonomous agents and agentic workflows: architect orchestration, integrate and optimize frontier LLMs, drive model fine-tuning and evaluation, mentor engineers, and translate business goals into safe, reliable, scalable AI-powered product experiences.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Graduate research interns will own end-to-end projects to build customer world models and proactive intelligence: developing representation learning, foundation model, RL/agentic methods, evaluation frameworks, and shipping research into production to improve customer decision-making.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.