Top Machine Learning Jobs in Toronto

Leads software engineering teams and owns technical delivery across XR/MR applications, motion-capture pipelines, embedded systems, and cloud SaaS products. Responsibilities include people management, architecture, design reviews, code quality, SDLC delivery, DevOps, AI-assisted development tooling, process improvement, executive reporting, and client-facing technical support. The role requires strong software engineering fundamentals, production architecture ownership, leadership experience, LLM and machine learning knowledge, and hands-on AI tool integration.

Leads OPTrust’s strategic innovation portfolio and total portfolio analytics within the CIO’s office. Builds external partnerships, identifies disruption opportunities, advances AI and quantitative investment capabilities, and develops frameworks for portfolio risk, research, and decision-making. Collaborates with investment teams and committees, monitors portfolio exposures and market impacts, communicates complex investment concepts, and mentors future investment leaders.

Develop and prototype AI applications for market and counterparty credit risk policy development and management. Collaborate with stakeholders to translate business problems into analytical solutions, conduct exploratory data analyses, identify actionable insights, and automate workflows. The role involves experimenting with LLMs, generative AI, prompt engineering, MCP integrations, machine learning, and deep analytics while communicating technical findings to non-technical stakeholders.

Develop and prototype robotic hardware for expressive, interactive consumer robots. Design mechanical systems, electronics, PCBs and embedded integrations, collaborate with ML/software engineers, and iterate on perception and interaction hardware for production-ready robots.

Senior Product Designer on Enterprise Experience Design team creating end-to-end enterprise/B2B tools for content planning, production, launch, and promotion. Lead user research, prototyping, visual and interaction design, usability testing, and cross-functional collaboration with PMs, engineers, data scientists, and stakeholders. Drive product strategy, design systems, and scalable solutions for global users while working PST hours and traveling quarterly to company offices.

Lead strategy, roadmap, and delivery of Authorization products (Payment Host, Encryption Platform). Partner with engineering, data science, card brands, and stakeholders to build secure, compliant, scalable payment infrastructure, define OKRs, manage product performance, and drive go-to-market execution.

Lead strategy and execution of personalized, data-driven Blue Rewards communications. Oversee segmentation, channel optimization, compliance, and performance measurement. Drive adoption of AI, predictive modeling, and automation to improve engagement, profitability, and NPS while managing and developing a small analytics team and influencing senior stakeholders.

Lead product for shared matching and personalization infrastructure: define AI/ML roadmap (retrieval, embeddings, ranking), own evaluation and experimentation, coordinate cross-functional teams, and measure improvements in match quality and relevance.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Design and develop data products for Commercial Insurance by converting business needs into user stories and technical specs. Evaluate data sources, support Agile development, scope projects, plan and review tests, ensure data documentation, governance, and integrity, and share analytics best practices with stakeholders.

Lead IT risk consulting engagements for global banks: assess IT general controls and application controls, identify risks, develop remediation, present findings to stakeholders, manage projects and teams, mentor staff, and support business development.

Design, develop, and productionize AI/ML features and infrastructure. Implement and optimize models, build APIs and microservices, monitor and tune performance, mentor engineers, and support full lifecycle deployment and A/B testing.

Lead full-cycle technical recruiting for Data/ML, Platform, and Engineering; build predictable pipelines, design structured interview processes and scorecards, run high-signal screening, partner with senior leadership, and improve recruiting workflows to hire and retain high-performing talent.

Lead end-to-end product design for real-time insights, reporting, and analytics in a legal SaaS product. Translate complex data and domain logic into scalable, accessible UI patterns, prototype AI-powered features, conduct customer research, and partner with product and engineering to drive strategy, launches, and iterative improvements.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Drive high-priority, cross-functional initiatives to scale the company: diagnose problems, create project plans, build decision tools, support sales/marketing/product, manage pilots, and partner with leadership on metrics and quarterly planning.

Use legal training to create and evaluate AI training data: perform legal research and analysis, provide annotation and feedback, support senior team members, communicate insights, participate in stakeholder presentations, and advocate for Responsible AI.

Lead end-to-end improvements to pricing ML systems: analyze market and vehicle data, design features, build/validate/deploy production ML models, run experiments, and partner cross-functionally to improve margin and conversion. Expand work into lending, fraud, logistics, search, and recommendations as needed.

As a Data Scientist, develop machine learning models to support trading strategies, collaborate with teams, analyze data, and improve trading performance.

The Senior Applied Scientist will lead research projects in AI/ML for various legal and taxation solutions, focusing on developing scalable AI capabilities while mentoring junior team members.

Lead design and build of high-scale, low-latency backend systems for premium ad formats. Define architecture for unified serving and rendering, enable experimentation and optimization, collaborate with Product/Design/Data Science, mentor engineers, and drive availability, scalability, and roadmap for ad formats.

Lead delivery of ML feature platform initiatives: design, build, and operate backend systems for feature creation, storage, backfilling and serving. Collaborate with product, design, and analytics, own quarterly goals, improve platform reliability and code quality, mentor engineers, and support on-call and operational monitoring.