Top Machine Learning Jobs in Toronto

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

Lead cross-functional hardware product teams from concept to deployment for machine vision and RFID solutions. Drive program performance, quality, regulatory compliance, cost, schedule and budgets while aligning EE, ME, software, operations and commercial teams. Manage risks, releases, integrations, pilots, and go-to-market transitions; engage partners, stakeholders and senior leadership using Agile practices and tools.

Lead Memory product vision and roadmap to deliver personalization across discovery-to-conversion surfaces. Define metrics and experimentation strategy, translate memory signals into user-facing features, write PRDs, coordinate cross-functional teams, champion privacy and responsible personalization, and drive measurable improvements in retention, engagement, and GMV.

Drive revenue by selling Splunk to large enterprise accounts. Build pipeline, close license/support/service deals, expand relationships, orchestrate complex multi-stakeholder deals, leverage partners and channels, and provide cross-functional feedback to support growth.

Lead enterprise client partnerships to deploy Valence's generative AI coaching platform. Drive renewals, expansion, and usage targets; embed programs within client organizations; guide behavior change; influence product roadmap; partner with Sales and Operations; and build fluency in generative AI, ML ethics, and industry-specific enterprise deployment.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Lead the Platform Software Engineering team building and scaling core data infrastructure for research, market-making, and risk platforms. Hands-on role combining individual contributor work, system architecture, cross-functional project leadership, and people management while collaborating with Research and Business Operations.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

The Principal Enterprise Architect for the AI Platform leads technical strategy for AI systems, defining architectures, governance, and standards while influencing senior leadership and mentoring engineers.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Lead Float Intelligence end to end, owning the AI product roadmap, technical architecture, model and LLM decisions, evaluation, infrastructure, and customer-facing delivery. Manage product and data/ML engineers, build shared AI platform capabilities, launch products from proof of concept to production scale, and partner with product teams. The role requires strong product judgment, hands-on production AI experience, people leadership, and the ability to make high-impact technical and commercial decisions in an exploratory environment.

Serve as the primary technical advisor for SMB sales opportunities, partnering with Account Executives throughout sales cycles. Lead technical discovery, product demonstrations, proof-of-value engagements, competitive evaluations, security questionnaires, RFPs, and technical documentation. Advise IT, security, developer, DevOps, and executive stakeholders on identity, endpoint, SaaS, secrets, cloud, and AI security solutions. Collaborate cross-functionally to improve demos, playbooks, enablement, and customer education.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Leads global strategic sourcing and procurement for digital technology and Food & Nutrition categories. Manages $125M+ in spend, SaaS and professional services suppliers, sourcing strategies, RFPs, contracts, supplier governance, performance, risk, and cost optimization. Partners with senior stakeholders, Legal, Infosec, sourcing teams, and suppliers to support digital transformation, standardization, automation, and commercial objectives.

Lead strategy and delivery of AI-powered capabilities across Employ's recruiting platforms. Define product vision, prioritize roadmap, partner with engineering, design, and data science, ensure responsible AI practices, measure product success, and drive adoption to improve recruiter productivity and customer outcomes.

Lead product marketing for assigned portfolio: develop positioning/messaging, drive launches, enable sales, create content, gather customer and competitive insights, mentor junior team members, and measure adoption and growth.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

The Fractional CTO will lead AI-focused projects, manage technical teams, and guide startups in technology development and strategic planning.

Design and develop ML platforms, frameworks, and services; collaborate with data scientists on algorithms and simulations; build model hosting and integration services; create data pipelines and monitoring tools for large datasets and ML applications.

Lead marketing measurement and analytics across campaign, channel, and funnel; own attribution and forecasting; apply ML/LLM techniques for propensity scoring, forecasting, anomaly detection, and automated insights; manage marketing data stack (Salesforce, MAP, warehouse); build dashboards, deliver executive analyses, and mentor analysts.

Build, deploy, and maintain machine learning models on large-scale datasets. Extract, clean, and analyze numerical and textual data. Develop model pipelines, run experiments, optimize business strategies, and evaluate new data sources and technologies.

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.