Top AI Jobs in Toronto

The Senior Specialist in Product Management will lead market analysis, support planning for Security Solutions, and collaborate with cross-functional teams to enhance product performance and customer satisfaction.

Build and enhance AI and data solutions for insurance by collaborating with data architects, scientists, and engineers. Use AI-first tools and agents, design prompts, prototype generative AI, review AI-generated code for security and quality, track production trends, document patterns, participate in code reviews, and assist senior staff to scale AI capabilities across the platform.

The Data Scientist will design and build machine learning solutions, optimize for business value, and guide customer success through technical expertise and collaboration.

Perform and develop pipelines for large single-cell RNA-seq datasets (>3,000,000 cells) including alignment, QC, clustering and annotation. Apply advanced analyses beyond basic differential expression, integrate clinical and transcriptomic data using machine learning/AI, visualize results, mentor graduate students, manage archived datasets, and use the UHN high-performance computing cluster to support translational HBV research and industry collaborations.

Lead development and scaling of machine learning and deep learning solutions using big data to generate business insights and improve customer experience. Design predictive models, perform large-scale data analysis and data wrangling, evaluate AI/ML feasibility, and collaborate with product and analytics teams to operationalize models. Provide technical consulting, influence stakeholders, and apply risk and ethical frameworks while independently handling non-routine problems and broader analytics responsibilities across business units.

Lead enterprise-level analytics and ML efforts to build predictive models, mine large structured and unstructured data, and provide data-driven recommendations to senior stakeholders. Collaborate with product teams to design AI/ML solutions, advance data strategies, implement big data and visualization tools, and manage ambiguity while applying risk management and ethical considerations.

As an Applied Scientist, you will push the limits of AI/ML in home valuation, tackling diverse ML challenges and optimizing decision-making with data.

The Engineering Manager leads Data Engineering teams, developing scalable data solutions and collaborating with stakeholders to drive McCain's digital transformation.

The Audit Manager II oversees audits related to financial crimes, manages a team, conducts risk assessments, and ensures regulatory compliance while enhancing operational performance.

Lead and own the end-to-end Registry design experience, set vision and KPIs, hire and develop a small design team, use AI-native workflows to prototype and ship cross-platform consumer products, partner with PMs/engineers/analytics, and scale design systems and processes to drive measurable business impact.

Lead design and oversight of financial controls and governance for Sponsor Financial Services. Establish control inventories, monitor control effectiveness, manage remediation, assess operational risk and GL impacts, produce executive dashboards and analytics, lead root-cause analysis, partner with Finance, Compliance, Internal Audit, and drive continuous improvement and automation of controls.

Lead controllership for Capital Markets: advise stakeholders on new products/systems, perform complex accounting investigations, ensure IFRS-compliant financial reporting, drive analytics and automation projects (Alteryx, RPA, AI), and implement continuous process improvements and controls.

As an AI Engineer, you will develop and maintain data pipelines, manage data integration, and enhance data quality for analytics projects.

Build and evolve core AI platform capabilities: design shared execution patterns, APIs, and backend services to support multi-step GenAI workflows, model integrations, and scalable production systems. Collaborate with AI engineers, data scientists, and product partners to turn emerging AI use cases into reliable, extensible platform features.

The role involves leading product development for Video-based Safety using AI and ML, defining roadmaps, and collaborating with engineering and sales teams to enhance safety solutions.

Lead AI strategy adoption across Zynga by partnering with cross-functional teams, evaluating and onboarding vendors, tracking AI KPIs, ensuring governance/compliance, expediting dependencies, and advising on vendor fitness, ethics, security, and legal risks to drive effective internal use of AI tools.

Lead the evolution of the recommendations engine system into a scalable distributed architecture, ensuring production stability and performance while mentoring engineers and collaborating with ML teams.

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 Software Engineer II, you'll develop generative AI applications, enhance sales operations through AI tools, and collaborate with engineering teams on a remote basis.

Lead large-scale, cross-functional engineering projects to deliver AI-native SaaS features. Own platform architecture for multi-tenant, cloud-native systems, drive engineering standards and developer tooling, mentor engineers, and operationalize LLMs, agent frameworks, and real-time data in production.

Lead multi-year vision, strategy, and roadmap for Webflow's agent surfaces and AI platform. Align cross-functional teams, set quality standards and guardrails for non-deterministic agent experiences, serve both developers and creators, prototype and ship product improvements (including contributing code), and mentor PMs while measuring and iterating on outcomes.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

Serve as a trusted advisor delivering end-to-end operations and digital transformation consulting for SMEs. Lead diagnostics, design practical technology-enabled solutions (ERP, MES, WMS, IIoT, AI), manage project delivery, drive measurable operational improvements, and support business development and thought leadership activities.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

Lead development of AI tools for systems engineering, define specifications and test scenarios, evaluate new technologies for R&D, support project planning and coordination, prototype and validate components, and collaborate across engineering and external partners to advance automotive AI and system solutions.