Top Machine Learning Jobs in Toronto

Embed as financeAI partner across Tax, Controllership, Treasury, FP&A, and Opco teams to identify, prioritize, prototype, and deliver AI and data science solutions. Translate finance problems into technical requirements, ship LLM/retrieval/agentic tools, drive adoption, measure ROI and impact, and help scale the AI finance capability and team.

Join a research-focused team to integrate AI (LLMs and autonomous agents) into mobile platform development. Build AI-driven code generation and agentic systems, design core mobile components, and collaborate across teams to deploy AI-enhanced developer tooling and monitoring for HarmonyOS and large-scale mobile ecosystems.

Design and develop data products for commercial insurance by analyzing new data sources, elaborating requirements and user stories, supporting project scoping and testing, ensuring data documentation, integrity monitoring, and governance, and collaborating with business and engineering teams to validate solutions.

The Applied Researcher will develop and deploy ML models and data pipelines for search relevance and query understanding, collaborating with teams to drive innovations in NLP and deep learning.

As a Research Engineer, you will develop and enhance AI algorithms for self-driving vehicles, collaborating with scientists to advance technology and support deployment in production systems.

Serve as the primary technical pre-sales lead for large enterprise accounts, defining technical strategy, driving ML/AI and data-driven adoption, building technical champions, coordinating virtual teams, and supporting sales to deliver business outcomes using big data and cloud platforms.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

The Data Product Manager will lead the development of enterprise data products by translating business needs into scalable data solutions, managing analytics projects, and coordinating with global teams to ensure efficient delivery and optimization of data initiatives.

Design, implement, and lead full-stack features spanning Angular front-ends, ASP.NET server APIs, background services, and SQL databases. Optimize SQL performance, integrate GIS and third-party data, prototype ML solutions, contribute analytics/reporting (Power BI/SSRS), mentor junior engineers, and champion CI/CD and engineering best practices in a remote-first environment.

Design, build, test, and deliver scalable machine-learning and NLP systems and large-scale data pipelines. Integrate ML models into production, write maintainable tested code, collaborate cross-functionally, and drive research-to-product development in an agile environment.

Design, implement, and maintain scalable, object-oriented test automation frameworks for UI and server-side microservices. Build production-grade test code, integrate AI/ML for self-healing and predictive analytics, and enable CI/CD-driven deterministic automation pipelines. Collaborate in Agile to shift testing left, validate APIs and databases, and analyze/resolve automation failures.

Lead and execute product strategy for Everyday Money: define product thesis and roadmap, ship product lines, run AI innovation lab for agentic consumer experiences, manage and develop PMs, partner with design, Credit, and external partners, and own business economics and compliance for durable retention and monetization.

The Lead Metallurgist will oversee resource evaluation, audit geometallurgy models, liaise with teams, ensure quality control, and mentor junior staff.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

Design and build an AI assistant and proprietary AI systems for residential property management. Own end-to-end development from ideation to production, maintain high code quality, ship product features, and lead cross-functional engineering efforts.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

As a Machine Learning Engineering Intern at Cresta, you'll design, develop, and deploy AI Agent solutions, collaborating across teams to implement cutting-edge AI technologies in practical applications.

As a Senior/Staff Machine Learning Engineer, you will design scalable data pipelines, implement ML algorithms, and collaborate with engineers and Data Scientists to optimize the digital advertising platform.

Independently validate and monitor machine learning models used for credit underwriting, credit risk, and fraud detection. Identify weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Partner cross-functionally to implement and maintain the Model Risk Management framework and support audit, compliance, and regulatory requests.

As a Machine Learning Engineer II at Affirm, you'll develop AI systems for automating customer operations and collaborate on building models while ensuring high-quality code production.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

Lead AI risk assessments, reviews, and monitoring to support AI governance at a financial institution. Evaluate internal and third-party AI solutions, maintain an enterprise AI inventory, analyze controls and risk metrics, build dashboards and reports, and provide quantitative and advisory support across stakeholders to enable responsible AI adoption and regulatory readiness.

Lead digital transformation of global supply chain systems by designing AI-driven workflows, automation, APIs, and data pipelines. Optimize procurement, manufacturing, logistics, and planning through predictive analytics, Power BI reporting, and scalable integration solutions while partnering cross-functionally to drive adoption and continuous improvement.

Design and build predictive models and machine learning solutions using big-data technologies. Mine structured and unstructured data, develop end-to-end scoring pipelines, collaborate with product teams and stakeholders, and provide data-driven recommendations to senior leaders to improve customer experience and business outcomes.

Design and build ML and deep learning models, perform large-scale data analysis and EDA, collaborate with engineers and product teams on ETL and data solutions, provide data-driven recommendations to business stakeholders, and scale analytics into production-ready solutions.