Top Machine Learning Jobs in Toronto

Leads Vanguard Canada's digital marketing strategy across paid, owned, and earned channels. Oversees customer acquisition, engagement, retention, personalization, analytics, marketing technology, paid media budgets, agency partnerships, and regulatory-compliant communications. Builds integrated digital experiences for investors and advisors, partners with data and technology teams, evaluates emerging AI and marketing solutions, and develops a high-performing digital marketing team.

Designs and maintains observability systems for AI models and autonomous agent workflows. Responsibilities include LLM tracing, automated quality evaluations, drift and performance monitoring, token and inference cost optimization, agent governance, anomaly detection, alert management, and incident response. The role develops machine learning systems, experiments, algorithms, and production tooling while using Python, telemetry queries, and observability platforms to diagnose and remediate AI failures.

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.

Develop and deploy advanced machine learning models for personal banking marketing, risk management, and strategy optimization. Analyze large structured and unstructured datasets, perform feature engineering, and apply deep learning and large language models. Productionize solutions using Python, PySpark, PyTorch, SQL, cloud platforms, and MLOps practices. Conduct research, communicate findings, manage stakeholders, document work, and ensure regulatory, governance, risk, and business requirements are met.

Conduct bioinformatics research on large single-cell RNA sequencing datasets related to hepatitis B and liver immunology. Develop alignment, quality control, clustering, annotation, visualization, machine learning, and AI analysis pipelines. Integrate clinical and transcriptomic data, maintain research datasets, use high-performance computing resources, communicate findings to bench scientists, and mentor graduate students. The role supports clinical trial research, industry collaboration, and academic publication.

Defines and evolves data and AI product portfolios, setting product vision, roadmaps, requirements, priorities, success metrics, and responsible AI practices. Leads discovery, experimentation, validation, deployment monitoring, and continuous improvement while balancing business value, technical feasibility, security, compliance, data quality, and user experience. Collaborates with multidisciplinary technical and business teams, communicates recommendations and risks to stakeholders, and identifies opportunities involving generative AI, intelligent agents, RAG, automation, advanced analytics, and data platforms.

Designs and evolves secure, governed, production-ready AI and generative AI architectures, including RAG, intelligent agents, vector search, and API integrations. Evaluates platforms, models, cloud services, and orchestration frameworks; defines security, privacy, performance, and operational requirements; guides MLOps and LLMOps practices; supports prototypes and MVPs; documents architecture decisions; and advises business and technology stakeholders within a regulated financial services environment.

Conduct research and develop innovative machine learning solutions for enterprise banking applications. The role provides AI/ML thought leadership, applies advanced techniques to generate business insights, collaborates with data and engineering teams, and leads research and development in an AI subfield. Responsibilities include designing analytic solutions, conducting research, publishing findings, mentoring colleagues, managing risk and governance, and partnering with business stakeholders.

Develop and operationalize generative AI, Copilot, and agentic solutions for Finance. Partner with stakeholders to identify use cases, prepare data and semantic models, engineer prompts and retrieval approaches, evaluate solution quality, analyze datasets, communicate insights, and monitor deployed solutions. Support analytics modernization, Alteryx workflows, responsible AI practices, and cross-functional implementation from problem definition through operationalization.

Lead the strategy, development, launch, and optimization of AI-powered insights, recommendations, and adaptive learning capabilities. Partner with engineering, data science, design, customers, and commercial teams to conduct research, define roadmaps, develop experiments, measure business outcomes, and improve adoption and retention. Translate complex customer needs into product requirements while ensuring data quality, effective AI evaluation, and cross-functional alignment.

Develop and improve novel mass spectrometry applications, evaluate research opportunities, and mature innovations into commercial technologies. The role applies advanced expertise in instrument control, AI, and software development, collaborates cross-functionally, supports customer and marketing initiatives, evaluates business opportunities, and represents the company at conferences. Candidates need an advanced science degree, over 10 years of mass spectrometry experience, a strong publication record, and demonstrated research innovation.

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

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.

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.

Develops and maintains software applications across the full lifecycle, translating business requirements into technical specifications, writing and testing code, troubleshooting systems, and managing releases. The role focuses on automation, AI, DevOps, cloud technologies, microservices, Salesforce, AWS, CDK, Python, observability, and machine learning. It also supports system design, security standards, integration, performance, documentation, and technology evaluations while collaborating with functional teams and providing technical consulting.

Designs and conducts experiments on OLED devices and advanced materials, operates and maintains R&D equipment, analyzes and presents experimental data, collaborates with software and facility engineers, troubleshoots equipment, documents results, and supports safe laboratory operations.

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.

Senior Product Manager responsible for product lifecycle management, market and competitive analysis, customer advocacy, business case development, product offerings, KPIs, NPI processes, pricing, positioning, go-to-market strategy, distribution, product launches, and stakeholder training. The role requires software product management experience, collaboration with technical and business teams, and familiarity with AI-driven tools and enterprise mobile computing solutions.

Own the vision and roadmap for a unified data platform, aligning product, engineering, and data teams around shared architecture. Define APIs, data models, integrations, governance, security, and core platform services. Engage internal stakeholders and external clients, manage third-party data providers, evaluate AI tooling, drive platform consolidation, and establish adoption and success metrics while balancing immediate needs with long-term scalability.

Leads AI risk assessments, risk tiering, governance approvals, compliance reviews, lifecycle monitoring, risk remediation, and executive reporting. Partners with Privacy, Security, Legal, Risk, and business teams to embed responsible AI practices, regulatory requirements, enterprise controls, and governance standards throughout AI development and deployment.

Leads GS1 Canada’s enterprise business intelligence, analytics, market intelligence, KPI framework, and AI-enabled transformation. Builds and manages the analytics function, develops predictive and decision-oriented insights, supports executive and Board reporting, and partners cross-functionally on subscriber growth, retention, adoption, performance, and market opportunities. Establishes governance, responsible AI practices, measurement standards, and team capabilities while translating complex data into strategic recommendations.

Manages enterprise AI risk and compliance across the AI lifecycle. Leads risk assessments, tiering, governance approvals, compliance reviews, control monitoring, risk-register maintenance, remediation, and reporting. Partners with Privacy, Security, Legal, Risk, and business teams to identify and mitigate AI risks, while improving governance frameworks and enabling responsible innovation.

Leads 6–8 engineers building and operating Lyft’s high-scale dynamic pricing and ride offer selection systems. Owns roadmap execution, architecture, model productionization, reliability, SLAs, incident escalation, technical debt, hiring, performance management, and team development. Partners with Product, Science, Analytics, and senior stakeholders to balance technical, product, and business priorities.

Manages stress testing analytics, enterprise reporting, and risk data initiatives. Aggregates, maps, reconciles, validates, and analyzes large datasets; consolidates model results; applies analytical adjustments; and transforms complex outputs into visualizations and presentations for senior stakeholders. The role supports credit risk, loss forecasting, lending products, and stress testing while using Power BI, Excel, and PowerPoint, with potential Python, SQL, and SAS applications. It also provides analytical support, recommendations, and data-driven insights across technical and business teams.