Top AI Jobs in Toronto

Leads cybersecurity sales across Canada by developing go-to-market strategy, driving enterprise revenue, recruiting and coaching sales executives, managing territories and quotas, maintaining forecasts, and closing C-level opportunities. The role partners with ServiceNow teams, solution consultants, marketing, professional services, and channel partners to accelerate pipeline, expand customers, and establish the integrated security platform as a market standard.

Lead and grow McCain's Forward Deployed Engineering (FDE) practice for Manufacturing within the Yellow AI portfolio. Manage hiring, assignments, performance, and development of senior FDEs; set standards, playbooks, and practices; personally lead select AI engagements; partner with platform, engineering, governance, and business leaders to drive adoption, measurable impact, and compliant, responsible AI deployments. Provide executive representation and resolve resource conflicts while embedding with teams for coaching and execution.

Lead Mastercard's AI thought leadership: monitor AI/data trends, produce research and executive content, manage end-to-end content lifecycle, measure program impact, and represent the company at events while partnering with product, engineering, marketing, and executives.

Lead BET's portfolio strategy and run rapid 2–4 week venture-style diligence sprints to source, evaluate, and transition new growth opportunities into execution. Define investment theses, sponsor opportunities through validation and scale, and manage a small team of senior analysts while partnering with Product, Technology, Foundry, M&A, and go-to-market stakeholders. Leverage automation and AI to expand coverage and improve prioritization without proportional headcount growth.

Build and own core components of an LLM-powered agent platform: orchestration, tool integrations, APIs, knowledge/memory infrastructure, trust & safety, and evaluation tooling. Partner with Operations to translate workflows into agent capabilities, mentor teammates, and ship production-ready, scalable AI-native systems.

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.

Lead design and delivery of enterprise automation, integrations, and AI-driven workflows. Architect scalable integrations across SaaS platforms, build conversational self-service and agentic experiences, enable AI/LLM-based automation, and partner with security, infrastructure, and product teams to ensure resilient, maintainable enterprise platform solutions.

Manage a team building and maintaining New Relic’s GTM revenue automation engine. Integrate Salesforce, Clay, Rox, Boomi, Salesloft, and related systems; build AI-enabled workflows for lead scoring, routing, alerts, and outreach; maintain data pipelines, data quality, dashboards, and forecasting; and lead technical projects from requirements through adoption. The role requires hands-on integration engineering, Python/API expertise, GTM systems knowledge, cross-functional collaboration, and experience translating revenue data into business insights.

Own the Retail AI GTM Pod and deploy AI applications that improve sales and onboarding productivity. Lead solution sourcing, build-versus-buy decisions, roadmap prioritization, agile development, systems integration, data validation, application security, monitoring, and production support. Partner across Revenue Operations, Marketing, Business Technology, and Engineering to scale AI solutions. Develop enablement and adoption programs for nontechnical users while building secure, reliable AI pipelines and integrations with Salesforce, Snowflake, and Taskray.

Design and develop Large Language Model-based applications within Pharma R&D, focusing on client collaboration and technical excellence while ensuring production readiness and performance optimization.

Test and validate AI models and software for automotive use cases: generate ground truth, build annotated datasets and testing frameworks, evaluate technologies, support R&D engineers, coordinate projects and outsourced work, and collaborate with internal and external partners to drive innovation and product readiness.

Supports the Total Fund Risk & Analytics team through investment risk reporting, monitoring, quantitative analysis, research, and development of enterprise risk tools. Responsibilities include enhancing analytical capabilities, responding to on-demand requests, supporting risk modeling, exploring AI applications in investment risk, and collaborating across departments to build analytics tools for investment decision-making.

Own outbound top-of-funnel activities for skilled nursing or home health and hospice accounts. Generate qualified meetings through cold calling, LinkedIn, email, creative campaigns, and industry events. Build contact lists, messaging, and multi-touch sequences from scratch; use AI and buying signals to prioritize accounts; and collaborate with Account Executives, RevOps, and Marketing to develop territories and sales plays.

Designs, develops, tests, and deploys scalable machine learning and deep learning systems. Builds and enhances data pipelines and lakes, trains and monitors models, prevents model drift, optimizes algorithms, and transforms unstructured data into useful insights through applications such as image tagging and text-to-speech. Performs statistical analysis, interprets results, and collaborates with analytics professionals to automate and scale decision-support solutions.

Coordinates clinical research across lower-limb preservation, prosthetics, amputee, and geriatric rehabilitation programs. Responsibilities include protocol development, study start-up, ethics submissions, participant recruitment and consent, study visits, data collection, database maintenance, quality assurance, and research workflow development. The role collaborates with clinicians, researchers, rehabilitation specialists, program leaders, and academic partners while supporting digital health and innovation initiatives.

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.

Provide commercial legal support for global SaaS deals: draft and negotiate MSAs, SLAs, NDAs, SOWs and AI/data privacy terms; advise Sales, Dealdesk, Partnerships and Finance; create templates, run contract reviews, support RFPs and reseller agreements, and improve contract management processes.

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.

Analyze financial and accounting datasets to identify trends, variances, KPIs, and profitability drivers. Build dashboards, data models, and automated reports using Power BI, Tableau, and Excel. Extract and transform ERP data, support budgeting and forecasting, provide audit support, and improve reporting through automation, AI, and predictive analytics. Collaborate with finance and accounting teams to deliver actionable insights for business decisions.

Leads AI and digital transformation initiatives by owning product vision and roadmaps, identifying high-impact automation opportunities, ensuring AI governance and compliance, managing adoption and change, and tracking KPIs and ROI. Partners with operations, marketing, customer success, finance, IT, legal, compliance, security, regional teams, patients, and associates to improve digital experiences, operational excellence, competitiveness, and supply-chain workflows.

Lead the end-to-end lifecycle and ecosystem strategy for NVIDIA’s RAN Digital Twin portfolio. Define product requirements, architectures, roadmaps, value propositions, and go-to-market plans for telecom customers. Analyze competitors, engage partners and technical leaders, collaborate with engineering and field teams, and enable sales adoption. The role requires deep wireless communications, AI-native RAN, simulation, high-performance computing, and software product management expertise.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Supports OTC derivatives documentation across interest rates, foreign exchange, equities, and commodities. Reviews confirmations and ISDA documents, analyzes derivative structures and booking logic, resolves discrepancies, and ensures legal documentation reflects transaction economics and regulatory requirements. Partners with Legal, Trading, Risk, Technology, Compliance, and Operations, while mentoring colleagues, improving controls, influencing documentation standards, and contributing to automation and operating-model initiatives.

Design, build, and scale production GenAI services (MCP Server/Gateway, AI Agent). Own end-to-end features, ensure reliability, performance, and security, use AI-assisted development tools, collaborate with product/design, and mentor teammates while producing documentation and monitoring systems.

Conduct fundamental research in video generation, video editing, and 3D computer vision. Develop state-of-the-art AI and computer vision algorithms, create research prototypes, solve real-world problems, publish in leading conferences, integrate innovations into Huawei products, and supervise interns within collaborative R&D projects.