Top Machine Learning Jobs in Toronto

Leads enterprise wholesale AIRB credit risk modeling, including BRR/PD, LGD, and EAD models for commercial portfolios and regulatory capital. Oversees quantitative risk research, stress testing, scenario analysis, model risk management, regulatory compliance, and model development innovation using AI. Partners with executives, regulators, and risk stakeholders, manages a highly skilled team, develops talent, and influences enterprise-wide risk and business decisions.

Develop and operationalize cyber threat intelligence capabilities by collecting, analyzing, and disseminating actionable intelligence. Produce reports, advisories, alerts, briefings, and recommendations for technical and executive audiences. Support incident response and threat hunting, manage intelligence workflows and IOC lifecycles, conduct adversary and trend analysis, and build information-sharing partnerships. Apply AI and machine learning to improve collection, correlation, detection, and early warning while advising security teams on intelligence-driven resilience improvements.

Develop and operationalize cyber threat intelligence capabilities by collecting, analyzing, and disseminating actionable intelligence. Produce reports, advisories, alerts, and briefings; support incident response and threat hunting; manage intelligence processes, IOCs, and TIP governance; and improve intelligence-sharing workflows. Apply AI and machine learning to threat analysis, adversary profiling, IOC generation, and detection workflows while validating accuracy and responsible use. Advise security teams and senior stakeholders on emerging threats, business impact, and mitigation strategies.

Develop scalable, production-quality software for an autonomous vehicle platform. Responsibilities include building tools and frameworks for AV development, integrating web platforms with vehicles, creating customer-system integrations, deploying software, and collaborating with engineers, research scientists, commercial teams, and designers. The role offers hands-on experience in requirements identification, software development, and production deployment.

Leads Capital Markets client analytics, performance management, financial reporting, and CRM data integration. Provides executive insights on profitability, revenue, planning, performance drivers, and resource allocation. Oversees analytics models, segmentation, predictive initiatives, reporting governance, and technology integration while building a high-performing team. Partners with senior business and technology stakeholders to support strategic decisions, operational efficiency, client engagement, and business growth.

Leads Agile delivery across multiple teams or complex value streams, facilitating Scrum ceremonies, coaching teams, resolving impediments, managing dependencies and risks, and aligning stakeholders. The role oversees backlog and sprint goals, delivery metrics, release and change management, governance, reporting, and continuous improvement. Experience with analytics programs, cloud platforms, and tools such as Azure DevOps, Jira, Confluence, and Miro is valuable.

Own the discovery, direction, delivery, adoption, and continuous improvement of AI- and analytics-powered products for Commercial Banking. Translate business and user needs into product requirements, user stories, acceptance criteria, prioritized backlogs, and roadmaps. Partner with business leaders, data scientists, engineers, architects, and product teams to deliver measurable value, improve workflows, champion user experience, and align competing priorities.

Supports development of technology for Global Markets, including trading systems, analytics, automation, data pipelines, APIs, and visualization tools. Collaborates with traders, engineers, quantitative professionals, and business partners to deliver scalable solutions, improve workflows, and reduce operational risk. The role provides hands-on exposure to electronic trading, market structure, quantitative decision-making, and capital markets technology through a four-month, on-site summer work term.

Analyzes forecasting, merchandising, sales, and replenishment data using traditional analytics, statistical modeling, and machine learning. Develops predictive models, reporting, user tools, automation, and what-if scenario analyses. Partners with merchandising, forecasting, and promotions teams to ensure model outputs are explainable and actionable. Supports forecasting systems, manages work requests and projects, improves processes, and serves as a subject matter expert for forecasting initiatives.

Leads Pebl’s data strategy, infrastructure, security, governance, analytics, and AI integration. Owns scalable data pipelines, translates data into business insights, embeds AI across departments, communicates findings to executives and the board, and recruits and develops a high-performing data and AI team. The role requires hands-on expertise with Pebl’s modern data stack, extensive data or analytics experience, senior leadership background, and on-site work in Palo Alto Monday through Wednesday.

Analyzes merchandising, forecasting, and promotional data using traditional analytics, statistical modeling, and machine learning. Develops predictive models, reporting, user tools, automation, and what-if analyses to support assortment, forecasting, replenishment, and sales-driver decisions. Partners with cross-functional teams to ensure models are explainable and operational, manages multiple initiatives, supports forecasting systems, and serves as a subject matter expert on forecasting processes.

Own the architecture, performance, deployment, and scaling of the machine learning pipeline powering Stickerbox, an AI sticker printer for children. Responsibilities include optimizing latency, batching, caching, GPU utilization, model deployment and rollback, hosting decisions, safety enforcement, observability, autoscaling, queue management, release engineering, capacity planning, and on-call support for production generative ML systems.

Develop predictive and statistical models, integrate and validate data, maintain financial and profitability models, and deliver analytics, visualizations, and business recommendations to TD Auto Finance stakeholders. The role collaborates across risk, finance, capital, and product teams, identifies opportunities through data insights, presents findings to senior management, and supports strategic initiatives while improving analytical processes and data quality.

Design, develop, and implement secure, scalable full-stack software solutions for financial services clients. Translate business requirements into technical specifications, build front- and back-end applications, conduct code reviews, mentor junior engineers, and support testing, releases, QA, troubleshooting, and client deployments. Collaborate with cross-functional stakeholders while applying modern cloud, API, microservices, data analytics, and AI technologies.

Research and evaluate new ML/AI tools, build and deploy predictive models, create benchmarks and automation, perform quantitative and ad-hoc analyses on credit portfolio, and communicate results and recommendations to business partners and senior leadership.

Own the AI product portfolio by prioritizing use-case backlogs, defining product requirements and success criteria, maintaining roadmaps, and driving reusable AI assets across banking lines of business. The role translates business opportunities into product decisions, communicates portfolio capabilities and plans to stakeholders, and partners with leadership on ROI, capacity, and strategic priorities. Success requires strong product management experience, technical fluency in AI and analytics, stakeholder leadership, and financial services operations knowledge.

As a Lead Applied Scientist, you will define research strategy, mentor teams, and innovate AI/ML solutions for legal and other professional sectors.

Research and evaluate new ML/AI tools, build and deploy predictive models, create benchmarks and automation, perform quantitative and ad-hoc analyses, and partner with business stakeholders to inform credit risk decisions.

Own the Secure Analytics Access product portfolio, including vision, roadmap, backlog, lifecycle, onboarding standards, service operations, support processes, cost transparency, and chargeback mechanisms. Partner with security, architecture, engineering, analytics, operations, and business teams to enable secure, governed analytics adoption. Translate user and security requirements into scalable capabilities, establish operating models, remove impediments, and drive continuous improvement across enterprise analytics services.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Lead the design, development, deployment, and maintenance of scalable cloud-native platform capabilities for Walmart Connect’s sponsored search ad server. Build infrastructure, tooling, AI/ML frameworks, and distributed systems; conduct design reviews; drive software lifecycle activities; mentor engineers; influence architecture and technical decisions; and collaborate across teams to translate complex business needs into reliable, data-driven solutions.

Build and improve Firefox WebCompat internal dashboards, reporting tools, and broken-site screenshot capture pipelines. The role involves creating reliable data views for triage and metrics, automating screenshot workflows, collaborating with WebCompat and QA teams, and translating reporting needs into effective tooling. Familiarity with WebCompat workflows, Bugzilla, screenshot automation, and machine learning is preferred.

Design, implement, and deploy efficient adaptive machine learning systems that retrain or fine-tune models using production data. The role combines applied research with production engineering, including data strategy, model evaluation, online learning, reinforcement learning, observability, and real-world product development. The engineer will own data products, solve novel technical challenges, and collaborate closely with the founding team on research and product direction.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

Develop and implement safety framework methodologies for autonomous trucking readiness decisions. Build high-signal safety datasets across simulation and closed-course testing, establish feedback loops from road monitoring and safety events, validate safety evaluation pipelines, and create traceable safety artifacts. The role also requires cross-functional collaboration, technical mentorship, data analysis, statistical evaluation, and evidence-based safety case development for autonomous vehicles.