Top Machine Learning Jobs in Toronto

Develop and deploy machine learning and statistical models for commercial property and casualty insurance pricing. Responsibilities include transforming large datasets into insights, building claims and pricing models, managing the full model lifecycle, monitoring performance, improving reproducibility, maintaining data-mining tools, and communicating recommendations to business and technology partners.

Build and integrate robotic hardware and software for repeatable experiments, data collection, demonstrations, and learned-policy deployment. Develop calibration and diagnostic tools for coordinate frames, timing, and sensor quality. Create recording, teleoperation, and evaluation workflows while debugging failures across software, electronics, sensors, and mechanics. Improve system reliability through software enhancements and recovery procedures.

Serve as a technical leader supporting sales across enterprise, service provider, and public sector customers. Evaluate F5 solutions, explain technical and business trade-offs, conduct demonstrations and presentations, and collaborate with Sales, Marketing, Product Management, and Development. Provide expertise in application delivery, security, cloud, Kubernetes, containers, Linux, NGINX, APIs, AI infrastructure, and automation. Act as a technical evangelist, publish educational content, mentor solution engineering teams, and support strategic opportunities and product launches.

Own revenue growth across foundation AI labs, technology companies, and enterprises. The role leads account expansion, new-logo sales, multi-million-dollar negotiations, forecasting, and executive relationships while shaping product packaging, pricing, and new revenue lines. It also partners with Product, Engineering, Delivery, and Operations to commercialize AI data, evaluation, staffing, and agentic offerings, build repeatable sales motions, lead a growing commercial team, and represent the company in the frontier AI community.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Develops and maintains actuarial predictive models for Property & Casualty insurance using statistical and machine learning techniques. Collaborates with pricing teams, presents findings, supports model governance and compliance, writes maintainable Python code, monitors model performance, and manages multiple modeling projects with detailed documentation.

Leads a team of actuaries and data scientists delivering machine learning and analytical solutions for property and casualty insurance pricing. Sets the strategic direction for end-to-end modeling, oversees scalable frameworks and inference pipelines, partners with stakeholders and model governance teams, ensures regulatory compliance, and mentors team members across technical and business disciplines.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

Own and optimize digital customer success campaigns across onboarding, activation, adoption, retention, and expansion. Build scalable customer journeys, triggered programs, messaging, dashboards, and reporting. Analyze customer and product signals, monitor lifecycle metrics, conduct A/B testing and cohort analysis, and improve campaign performance. Collaborate with Customer Success, Product, Data, Marketing, Content, Support, and Operations teams to deliver measurable customer outcomes at scale.

Develop new and expansion sales opportunities for small and medium-sized fleet customers. Manage leads, prospect through phone, email, partner, and referral channels, qualify customer needs, conduct discovery conversations and product demonstrations, maintain accurate pipeline data in Salesloft and Salesforce, and advance opportunities through the sales process. Collaborate with revenue operations, partners, marketing, and business development leaders while building expertise in telematics, fleet management, competitors, and SMB fleet challenges.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Own the vision, strategy, roadmap, requirements, KPIs, and go-to-market execution for insurance-focused data products. Collaborate with engineering and data science teams on products, proofs of concept, and demos; communicate progress to executives; and ensure compliance with BFS regulatory, risk, and compliance requirements.

Manage financial crimes audit programs, assess internal controls and regulatory compliance, lead audit engagements as Auditor in Charge, oversee auditors, and manage audit planning, risk assessments, issue remediation, and strategic initiatives. The role requires expertise in AML, ATF, sanctions, KYC, customer risk, transaction monitoring, regulatory reporting, data analytics, technology, and financial crime models, along with strong leadership, communication, judgment, and stakeholder-management skills.

Lead the planning, coordination, governance, and delivery of cross-functional AI initiatives from ideation through deployment and scaling. Maintain program visibility, roadmaps, milestones, risks, dependencies, budgets, and executive reporting. Partner with investment, data, engineering, product, and business teams to prioritize use cases, operationalize AI solutions, support adoption, and achieve measurable business outcomes.

Leads the technical direction and execution of a machine learning team focused on personalization, relevance, ranking, search, and advertising. Designs, scales, and operates production ML systems, including training, evaluation, deployment, and serving infrastructure. Partners with product, data, and engineering stakeholders, drives architecture and system design decisions, mentors engineers, and uses AI tools to accelerate development and explore LLM-powered features.

Leads the technical direction of a machine learning team focused on personalization, relevance, ranking, search, and advertising systems. Designs and scales end-to-end ML architecture, including training, evaluation, deployment, and serving. Partners with product, data, and engineering stakeholders, drives system design tradeoffs, mentors engineers, and ensures reliable production operation at consumer scale. The role also encourages using AI and LLM tools to accelerate development and explore new ML-powered features.

Implement customer projects using Python, machine learning, and deep-learning tools for engineering, physics, CAD, CAE, and manufacturing workflows. Develop proofs of concept, analyze project data, train customer teams, and translate customer feedback into product improvements. The role requires strong engineering and machine-learning knowledge, simulation experience, customer-facing communication, and travel to US customer locations.

Manage complex corporate and commercial transactions, including acquisitions, investments, divestitures, restructurings, and joint ventures. Draft and negotiate commercial, finance, licensing, vendor, supply, and partnering agreements. Advise on global expansion, corporate structuring, risk management, technology transactions, privacy, AI, IoT, and regulatory matters. Support post-acquisition integration, product activities, and corporate, commercial, and employment matters while partnering with executives, business teams, governments, and outside counsel.

Develop and deploy machine learning and statistical models for Commercial Auto Pricing, claims prediction, customer optimization, and automation. Responsibilities include transforming large datasets into insights, partnering with Pricing, managing the full model lifecycle, monitoring model performance, maintaining data science tools, improving reproducibility, and communicating recommendations to technical and non-technical stakeholders.

Design and build scalable Java backend systems, architect microservices and REST APIs, write and review production code, lead development initiatives, establish coding standards, mentor engineers, and guide technical strategy. The role also involves evaluating technologies, collaborating with product stakeholders, and applying emerging AI and machine learning tools to improve engineering practices and system design.

Drive revenue through complex enterprise sales of cloud data security solutions. Identify customer needs, architect solutions, deliver proof-of-value engagements and technical demonstrations, and consult with executive stakeholders. Manage cross-functional resources, support partner channels, and represent the company at marketing events. The role requires cloud security, data protection, networking, database, IAM, encryption, regulatory, privacy, and technical communication expertise, with travel up to 50% within the assigned territory.

Develop and implement predictive, machine learning, and Generative AI solutions on Google Cloud Platform. Lead rapid proof-of-concepts involving document data extraction, fine-tuned models, and Retrieval-Augmented Generation. Collaborate with data scientists, ML engineers, and technology teams to integrate AI into business workflows, communicate technical insights to stakeholders, and apply responsible AI practices.

Lead the evaluation, training, fine-tuning, and production integration of machine learning and large language models. Build prompt engineering and model evaluation pipelines, optimize Elasticsearch-backed AI workflows, benchmark commercial and open-source models, and monitor production performance. Collaborate with product, engineering, and security teams to deliver safe, scalable AI capabilities while identifying improvements to data pipelines and engineering processes.

Lead the company’s AI product strategy, roadmap, and team. Oversee AI/ML optimization, generative AI, multi-agent systems, forecasting, attribution, experimentation, model governance, and deployment. Partner with engineering, data science, platform, GTM, and customer success leaders to scale reliable, explainable AI products, align executives, improve advertiser adoption, and drive measurable business outcomes.