Top AI & Machine Learning Jobs in Toronto, ON

Lead a small team and contribute hands-on to design, develop, deploy, and iterate AML and crypto risk detection machine learning models. Own technical roadmap, collaborate with Product and Engineering, ensure model quality, explainability, documentation, and governance, and drive production performance and outcomes across the model lifecycle.

Lead the development of AI and machine learning solutions at Mastercard, defining strategy and mentoring teams while ensuring compliance and enhancing consumer experiences.

Support building and scaling program governance for Mastercard's Agent Suite AI product. Design governance frameworks, technical workflows, reporting dashboards, and KPIs; drive automation, budget tracking, stakeholder alignment, and continuous improvement to ensure compliant, resilient, and efficient operations.

Lead architecture and productionalization of AI-native, large-scale, data-oriented product concepts. Design scalable solutions, implement AI systems with observability and cost optimization, establish responsible AI practices, and coach engineering teams across the product development lifecycle.

Lead AI-native engineering teams to design, build, test, and deploy large-scale, data-oriented, customer-facing applications using Generative AI, LLMs, and agentic patterns. Define engineering vision and AI-native SDLC, partner with product, leverage data technologies (e.g., Databricks), mentor talent, and drive 0→1 prototypes to production with strong backend (Java/Python) and frontend (React/Next.js) engineering.

Design, build, and maintain scalable data pipelines and ML model enablement at petabyte scale. Partner with data scientists to productionize features and models, ensure secure data governance, automate distributed workflows, implement testing/monitoring, and support incident response and performance tuning for fraud and identity risk systems.

Design and develop scalable data science capabilities to assess merchant risk, including feature engineering, entity resolution, prototyping ML solutions, building Databricks/Spark workflows, validating and monitoring models, and collaborating with Product and Engineering in an Agile environment.

Lead the design and development of machine learning models, collaborating with teams to optimize solutions and mentor junior members while ensuring technical excellence.

Lead go-to-market strategy and commercialization for Advanced Analytics and AI products in North America. Drive product launches, cross-functional alignment, market research, customer feedback analysis, training, and post-launch performance monitoring to grow the portfolio and support sales and client teams.

Lead advanced quantitative analysis of primary market research using methods such as Conjoint, MaxDiff, segmentation, and regression. Design experiments, validate models, interpret results, advise on methodology, partner with cross-functional teams, mentor junior analysts, and help build analytics standards and tools to support multi-market consulting engagements.