Top Machine Learning Jobs in Toronto

Leads Colliers’ enterprise data engineering strategy and a team of data engineering managers and engineers. Oversees scalable multi-cloud data platforms, microservices, data mesh and fabric capabilities, AI/ML pipelines, GenAI workflows, analytics, and business intelligence solutions. Establishes DataOps, governance, security, CI/CD, and reliability standards while managing platform operations, incident response, service levels, technology roadmaps, and cross-functional partnerships.

Coordinate cross-functional technical programs from ambiguous priorities through release. Define scopes, sequencing, workstreams, dependencies, ownership, and decision points; write technical specifications; run effective program cadences; report risks and progress to executives; and identify issues involving models, data pipelines, deployments, monitoring, and infrastructure before they impact deadlines.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Designs, develops, tests, and optimizes scalable machine learning and deep learning systems. Builds ML applications and algorithms, enhances data pipelines and lakes, analyzes large historical datasets, and monitors model performance to prevent drift. Converts unstructured data into useful insights through applications such as image auto-tagging and text-to-speech. Provides technical support and collaborates with analytics professionals to deliver repeatable decision-support solutions while adhering to BMO’s risk management policies.

Oversee AI risk across assigned portfolios, providing second-line challenge, regulatory and audit coordination, assurance oversight, and emerging regulatory intelligence. Lead regulatory examinations, audit responses, executive reporting, cross-functional workplans, governance routines, and risk escalation. Monitor global AI regulations and translate developments, audit findings, and industry practices into enhancements to AI governance frameworks, controls, and risk management processes. Influence business, technology, compliance, legal, and audit stakeholders while representing AI Risk with regulators and senior leadership.

Develops machine learning, deep learning, and predictive models using large structured and unstructured datasets. Mines data, identifies trends, evaluates AI/ML solutions, and creates scalable analytics processes to support business decisions, customer experience, and productivity. Collaborates with product teams, analytics professionals, and senior stakeholders to provide recommendations, shape roadmaps, and address complex business problems while managing risk within banking policies and regulations.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Develop and maintain retail pricing strategies, models, rules, and optimization scenarios to improve sales, profitability, competitiveness, and market share. Analyze pricing and promotional trends, identify anomalies, support web-scraped competitive pricing data, and build BI dashboards. Collaborate with merchandising, vendors, data engineering, and data science teams on data pipelines, tool enhancements, governance, and process improvements.

Leads Gore Mutual’s data science function, managing teams that develop, deploy, validate, and govern predictive, generative AI, LLM, and agentic solutions. Sets the analytics and AI strategy, oversees production ML infrastructure and model monitoring, ensures regulatory, ethical, privacy, and risk compliance, and identifies opportunities to improve underwriting, fraud detection, pricing, operations, and business growth. Partners with executives and cross-functional teams to translate analytical findings into strategic outcomes while fostering innovation, experimentation, and advanced data science capabilities.

Develop and maintain behavior-planning systems for autonomous vehicles operating in structured, low-speed airport environments. Responsibilities include designing planning algorithms, implementing scalable C++ and Python software, modeling multi-agent interactions, testing in simulation and real-world scenarios, optimizing performance and latency, documenting system designs, and collaborating with autonomy and engineering teams.

Leads strategy, governance, modernization, and operational excellence for enterprise platforms and workplace technology. Oversees Google Workspace, ServiceNow, ERP, web, desktop, workflow automation, and AI initiatives; integrates systems, reduces technical debt, establishes SLAs and analytics, manages vendors and budgets, partners with executives, and leads platform engineering and management teams.

Owns the strategy, roadmap, governance, and business outcomes for an enterprise fraud platform. The role prioritizes fraud controls, detection models, platform enhancements, and operational processes while ensuring regulatory compliance, resilience, and customer experience. Responsibilities include backlog and release management, UAT and operational readiness, stakeholder and vendor leadership, incident prioritization, platform performance optimization, and collaboration across Fraud Operations, Payments, Technology, Risk, and Compliance.

Lead and develop a team of software, machine learning, and infrastructure engineers. Grow technical leaders, manage performance and hiring, remove systemic bottlenecks, clarify priorities, and own team outcomes. This player-coach role requires staying technically engaged, shaping engineering culture and processes, and helping the team use AI effectively in software development. Experience building AI in production and managing senior or staff-level engineers is required.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

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.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Supports credit risk and account management scorecard development, monitoring, validation, and optimization. Analyzes customer, portfolio, and performance data; creates dashboards, reports, KPIs, and recommendations; performs statistical performance monitoring and assists with model validation and challenger testing. The role also contributes to reporting automation and decision-support tools while collaborating with Credit Risk, Fraud, AML, Product, Marketing, Operations, and Model Risk teams.

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 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.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Leads lab-in-the-loop biology programs that connect experimental design with machine learning model development. Oversees closed-loop experiments, model-output verification, perturbation studies, data generation, assay and model-system development, automation, metadata standards, CRO and vendor relationships, budgets, and cross-functional collaboration. Builds and mentors technical teams while aligning experimental and computational groups across a matrixed biotech organization.

Train and evaluate computer vision models for embedded deployment, build efficient C++ inference pipelines, and optimize latency, memory, power, and sustained performance. Integrate models with embedded Linux, test on representative hardware and data, and investigate errors across preprocessing, inference, and system integration. Lead candidates will set architecture and evaluation standards while guiding model and software development.

Train and fine-tune diffusion models, adapters, and pipelines for character-based sticker generation. Build provenance-aware training data pipelines and evaluation systems measuring character fidelity and output quality. Partner with brand teams to secure character approvals, ensure reliable outputs from spoken requests, and collaborate with serving and trust-and-safety engineers to launch safe products for children.

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.