Top Machine Learning Jobs in Toronto

Leads Mastercard’s Tech for Good initiatives, scaling AI, data, technology, and analytics for social impact organizations. Manages grants, partnerships, internal coalitions, proprietary tools, vendors, and cross-functional technical projects. Drives data- and AI-enabled decision-making, research translation, measurement and evaluation, grantee capability building, and thought leadership while advancing inclusive growth and financial health outcomes.

Own GPTZero’s student-focused product from discovery through launch and iteration. Lead user research, define requirements and success metrics, manage experimentation, and partner with design, engineering, and ML teams. Build product processes from scratch as the first product manager while translating AI model capabilities and limitations into trustworthy user experiences.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads a team of Technical Program Managers executing complex, multi-year technology initiatives, including cloud architecture updates, application convergence, integrations, and enterprise data migrations. Aligns Product, Engineering, Design, Data Science, and Risk stakeholders, resolves delivery bottlenecks, influences senior leaders, and establishes governance, metrics, release controls, and Agile operating practices. Coaches TPMs and improves delivery execution across a matrixed organization.

Lead the architecture and development of scalable data platforms and distributed processing systems. Establish enterprise data governance, quality, privacy, compliance, schema management, and lifecycle standards. Drive resilient, automated, and observable data pipeline strategies; evaluate technologies; review complex architectures; collaborate with business and technology stakeholders; and mentor data engineers in stream processing, data modeling, and MLOps.

Design, build, deploy, and operate production optimization applications using machine learning, advanced algorithms, APIs, data pipelines, and evaluation systems. Own system correctness, performance, testing, reliability, observability, and customer outcomes. Direct AI coding agents through precise specifications and review their implementations. Collaborate with product, design, engineering, and customers to define requirements, communicate risks, and influence the product roadmap.

Lead product strategy and roadmap for AI-native capabilities in CrowdStrike's Next-Gen SIEM. Collaborate with engineering, data science, research, marketing, sales, and support to design, deliver, and position AI-driven detection, investigation, and prevention features. Conduct competitive analysis, define use cases, and engage customers to inform product direction while supporting go-to-market and cross-functional execution.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Leads the design, development, deployment, and commercialization of enterprise AI capabilities for the ARGUS Intelligence Platform. Builds LLM applications, agentic workflows, intelligent data extraction, AI analytics, and domain-specific models. Establishes AI/ML engineering standards, governance, testing, deployment, and reliability practices; evaluates emerging technologies; guides architecture and model strategy; mentors engineers; and collaborates with Product, Engineering, Architecture, Data, and Analytics teams.

Leads statistical strategy for oncology drug discovery programs, influencing scientific and portfolio decisions from target identification through candidate selection and biomarker development. Applies statistical modeling, experimental design, AI/ML, data science, predictive analytics, and quantitative decision frameworks to translational and high-dimensional studies. Collaborates with multidisciplinary and external teams, develops analytics capabilities and decision-support tools, champions reproducible research, and leads publications and scientific presentations.

Lead and manage financial crimes audit program for high-risk areas, assess internal controls and regulatory compliance, plan audits, perform risk assessments, manage issues, oversee audit teams as Auditor-in-Charge, coordinate with other audit groups, and apply data analytics and emerging technologies to testing and reporting.

Leads the enterprise IT function across Canada, setting technology strategy, modernizing applications and platforms, advancing AI and automation, and strengthening cybersecurity, infrastructure, and IT controls. Oversees technology roadmaps, budgets, vendors, service management, governance, and portfolio rationalization while partnering with executive stakeholders. Manages IT leaders and develops talent, ensuring technology investments support business growth, operational excellence, security, and digital transformation.

Builds and implements scalable AI and machine learning solutions for capital markets finance and accounting operations. The role enhances data pipelines, develops production-like ML applications, validates and optimizes models, automates closing, reconciliation, reporting, and governance processes, and applies responsible human-in-the-loop AI practices. It also identifies business use cases, supports strategic initiatives, advises stakeholders, and drives operational efficiencies through AI tools.

Leads pharmacovigilance workflow expertise for Veeva Falcon, advising product and engineering teams on safety case processing, reconciliation, AI calibration, and compliance. Translates PV industry trends into automation strategies, engages early adopter customers, and develops complex scenarios to stress-test platform capabilities. Bridges hands-on drug safety operations with technical product development and executive customer engagement.

Lead data science initiatives supporting Wealthsimple’s Commercial team and AI Voice Agent program. Build reliable data models, develop and maintain machine-learning models, design experiments, conduct analyses, and create dashboards and metrics. Partner with Sales, Operations, Product, Design, Engineering, and leadership to improve sales processes and business outcomes. Mentor teammates and communicate insights to senior stakeholders.

Design, develop, test, deploy, and support secure cloud-based applications and microservices. Build Azure DevOps CI/CD pipelines, containerized Java systems, APIs, frontend applications, and RPA/ML automation using Azure services, Kubernetes, Power Platform, and Logic Apps. Monitor application performance, resolve deployment and production issues, conduct testing, document technical solutions, support incident response and root-cause analysis, and provide knowledge transfer while collaborating with business and IT stakeholders.

Lead the development and deployment of machine learning and statistical models supporting pharmaceutical commercial performance, marketing optimization, customer recommendations, segmentation, and targeting. Perform data mining, wrangling, feature engineering, A/B testing, and analytical pipeline integration. Partner with commercial, data governance, and global teams to identify opportunities, improve analytics capabilities, and communicate data-driven recommendations to technical and non-technical stakeholders.

Own the roadmap, quality bar, evaluation frameworks, and product outcomes for Caseware’s AI agents, agent builder, eval builder, and agentic memory. Partner with applied scientists, engineers, auditors, and accounting firms to validate outputs, define acceptance criteria, measure product health, and determine what ships. Translate practitioner workflows and failure modes into actionable requirements while monitoring AI advancements and coordinating delivery across technical teams.

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

Develops underwriting analytics and AI solutions for TD Insurance, contributing to strategy, roadmap execution, model performance tracking, and business recommendations. Partners with underwriting stakeholders, serves as an underwriting analytics subject matter expert, translates complex concepts for senior executives, and influences strategic decisions through research, modeling, and data analysis. The role also supports stakeholder alignment, team promotion, and innovative solutions across underwriting operations and strategy.

The Actuarial Analyst will analyze insurance claim reserve adequacy using standard actuarial methods, advanced reserving models, and machine learning. Responsibilities include supporting quarterly and year-end valuation reporting, maintaining monitoring reports, providing reserving insights into financial performance, partnering across teams, and improving processes. The role requires actuarial education, at least one year of experience, active pursuit of actuarial exams, strong analytical and communication skills, and proficiency in Excel and VBA.

Analyze claim reserve adequacy using actuarial methods and advanced machine learning models in Python. Support quarterly reserve committee and year-end valuation reporting, maintain internal and external monitoring reports, and provide financial performance insights from a reserving perspective. Partner with cross-functional teams to assess portfolio impacts and improve processes. The role requires actuarial exam progression, strong analytical and communication skills, and proficiency in Excel and VBA.

Leads enterprise Quality Engineering strategy, standards, governance, tooling, automation, and Center of Excellence development. Drives shift-left quality, AI-enabled testing, workforce transformation, maturity models, dashboards, and reusable testing practices across engineering teams. Establishes responsible AI testing controls covering model validation, bias, explainability, monitoring, and regulatory alignment. Influences senior leaders, vendors, and delivery teams while guiding automation roadmaps, risk-based testing, quality metrics, and adoption of modern engineering practices.

Leads modernization of private-markets investment workflows through technology, automation, data, and AI. Responsibilities include mapping and redesigning investment processes, evaluating and implementing AI solutions, establishing governance, driving adoption and change management, and overseeing data and AI value-creation initiatives across portfolio companies. Develops value-realization frameworks, KPIs, scorecards, dashboards, and reporting for senior management and investment committees while coordinating investment, technology, risk, legal, finance, and compliance stakeholders.

Leads enterprise architecture strategy for AI, data platforms, analytics, and governance. Designs scalable lakehouse, AI/ML, streaming, integration, BI, and data governance solutions using cloud technologies. Establishes architecture standards, leads reviews and transformation initiatives, guides build-versus-buy decisions, and ensures security and compliance alignment. Partners with executives and cross-functional technology teams, mentors architects, and drives standardization, innovation, performance, and solution quality across AI and data ecosystems.