Top Machine Learning Jobs in Toronto

Build machine learning, causal inference, optimization, and decision systems supporting customer acquisition, lifecycle engagement, marketing measurement, and conversion. Develop intent, conversion, lifetime value, and marketing allocation models; estimate incremental impact; and translate research into production systems. Partner with Marketing, Product, Engineering, and Sales while owning model evaluation and improvement.

Designs and positions telecom analytics solutions for network visibility, service assurance, customer experience, and operational intelligence. Responsibilities include architecting end-to-end data pipelines, applying 4G, 5G, and IMS expertise, integrating probe data and telemetry, enabling AI/ML and Generative AI use cases, and advising engineering and delivery teams. The role also develops reusable architecture patterns, manages solution risks, and supports cloud-native, real-time, and operational analytics implementations.

Supports sales, customer success, and product development for Mastercard Identity through data analysis, machine learning, rules-based fraud models, and proof-of-value testing. Advises customers on fraud workflows, analyzes data features, supports API integration QA, and provides product recommendations. Builds customer relationships, translates business needs into technical solutions, contributes to product specifications and training, and serves as a subject matter expert on identity data and products.

Leads end-to-end delivery of digital products and data-enabled solutions for R&D and Innovation. Translates scientific and business needs into technical requirements, architectures, integrations, and scalable solutions. Collaborates with product, data, AI, security, privacy, infrastructure, and vendor teams to manage backlogs, testing, releases, support, and continuous improvement. The role also evaluates responsible AI opportunities, establishes engineering standards, supports adoption, and monitors product performance and user feedback.

Lead a team of at least six engineers building enterprise developer platforms and tools. Architect AI-powered developer productivity capabilities, optimize development workflows, manage GitHub and CI/CD infrastructure, and improve internal libraries. Partner with engineering, product, and business leaders on technical strategy, mentor engineers, establish standards, and drive innovation across a large technology organization.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Build infrastructure and tooling for AI evaluation, including distributed execution, task-set validation, graders, human-review calibration, statistical reporting, online feedback loops, and offline-to-online analysis. Partner with product, engineering, data, and ML teams to create reliable evaluation workflows that support faster, statistically sound AI product decisions.

Build infrastructure and tooling for AI evaluations, including distributed scoring engines, task-set validation, grader systems, human-review calibration, statistical leaderboards, online feedback capture, and offline-to-online evaluation loops. Partner with product, engineering, data, and ML teams to deliver reliable, observable evaluation workflows that support faster and more trustworthy AI product development.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Leads multiple software engineering teams building foundational capabilities for customer-facing web and mobile applications. Responsibilities include people management, mentoring, roadmap execution, stakeholder partnership, architecture, and delivery of scalable enterprise platforms using cloud, microservices, streaming, data, and machine learning technologies. The role promotes engineering excellence, psychological safety, innovation, diversity, and agile collaboration while developing engineers and other managers.

Develops next-generation AI, machine learning, generative AI, and agentic AI solutions for engineering workflows. Responsibilities include researching techniques, evaluating approaches, building and validating production-ready applications, APIs, services, and data pipelines, and transitioning solutions to internal customers. The role also evaluates emerging technologies, supports R&D programs, coordinates innovation projects, analyzes engineering results, and collaborates with engineering teams, universities, suppliers, and strategic partners.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Build and operate scalable full-stack software and AI-powered products for IoT-driven physical operations. Responsibilities include defining technical strategy, developing backend services, APIs, frontend and potentially mobile features, transforming AI capabilities into customer products, partnering across functions, leading complex projects, mentoring engineers, and improving engineering standards.

Owns product vision and roadmap for Mastercard’s identity and payments data products. Responsibilities include prioritizing product development, creating data-backed business cases, aligning cross-functional stakeholders, defining requirements, managing risks, tracking delivery, representing customer needs, and developing future commercial opportunities. The role also involves mentoring product managers, managing globally distributed teams, and communicating with executive stakeholders.

Develop predictive models and machine learning solutions for fraud detection, customer behavior, financial risk, and marketing optimization. Analyze large transaction datasets using Python and PySpark, explore new data sources, and build data pipelines and visualizations to monitor model performance and data quality. The role involves end-to-end project ownership, cross-functional collaboration, and communicating technical concepts to non-technical audiences.

Drive pipeline and revenue growth through digital prospecting, lead qualification, relationship building, and SaaS sales. Engage prospective enterprise customers, manage inbound and outbound leads, use tools such as LinkedIn, 6Sense, Outreach, ZoomInfo, and Salesforce, and collaborate with account executives, sales engineers, channel partners, and marketing. The role includes booking discovery calls, creating opportunities, meeting funnel and pipeline KPIs, and closing transactional deals and upsells.

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.

Develop and execute brand strategies for rare disease therapies across their lifecycles. Drive commercial performance, launch readiness, market insights, stakeholder engagement, cross-functional collaboration, agency execution, and budget management. Use market research, analytics, AI, and real-world evidence to inform strategy, patient access, customer engagement, and business growth in the Canadian market.

Develop and validate machine learning and statistical models for consumer credit risk, fraud, portfolio management, IFRS 9 forecasting, and borrower behavior. Build explainable predictive models, automate analytical processes, identify quality data sources, document methodologies, and translate complex findings into actionable business insights. The role partners with cross-functional teams and balances model performance, interpretability, scalability, and commercial impact.

Machine Learning Interns will own scoped ML projects from problem definition through experimentation, implementation, testing, and communication. They will develop and evaluate models for discovery, advertising, and search using techniques such as NLP and graph-based modeling; analyze data; contribute to applied AI research; write maintainable code; and collaborate with engineering, product, and data teams.

Designs, builds, evaluates, and productionizes machine learning and GenAI solutions for Finance, Treasury, and Actuarial workflows. Responsibilities include developing forecasting, NLP, anomaly detection, RAG, summarization, and agentic systems; creating architecture and governance documentation; implementing evaluation, monitoring, guardrails, and remediation; partnering with engineering and business teams on deployment and UAT; and mentoring junior scientists.

Designs and productionizes machine learning, generative AI, and agentic solutions for actuarial and enterprise business workflows. Responsibilities include predictive modeling, forecasting, anomaly detection, retrieval-based solutions, evaluation, monitoring, governance documentation, and stakeholder adoption. The role partners with engineering teams on deployment, CI/CD, data quality, drift monitoring, UAT, and operational support while mentoring junior scientists and standardizing AI development practices.

Own the AI automations platform, including authoring, execution, evaluation, and improvement models. Build high-impact automations, enable product teams to ship efficiently, define platform metrics, and partner with AI engineering on agent infrastructure. The role requires customer discovery, technical decision-making, unit economics, and experience delivering adopted AI products and platforms.