Top Machine Learning Jobs in Toronto

Sell Agentforce to mid-market and commercial customers by sourcing, qualifying, and closing opportunities with Salesforce teams. Lead technical discovery, articulate AI agent and Data Cloud capabilities, craft POVs linking technical features to business outcomes, and collaborate cross-functionally to deliver seamless customer experiences.

Enterprise Account Executive responsible for selling Salesforce Agentforce and Data 360 solutions. Lead technical discovery, qualify and source opportunities, craft POVs, collaborate cross-functionally, articulate AI/data-driven value, and consistently exceed enterprise sales targets by mapping complex client needs to scalable Salesforce solutions.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Design, model, and test RF transmitters and receivers (700 MHz–10 GHz) for rugged handheld products. Lead RF system integration, EMI/EMC troubleshooting, lab test strategy, cross-functional debugging, and JDM collaboration. Investigate and apply AI/ML techniques to improve RF design and test efficiency; drive products from concept to production.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

The Senior Manager of AI and Innovation Communications will lead communications strategies for AI products, customer storytelling, and executive thought leadership, engaging with technology teams to craft impactful narratives.

Senior AI leader driving end-to-end delivery of AI solutions across Corporate Functions, focusing on fraud detection and financial crime prevention. Leads cross-functional teams, defines roadmaps, provides hands-on technical architecture and prototyping, ensures governance/security/compliance, promotes enterprise reuse, and drives stakeholder adoption and measurable business outcomes.

Lead AI assurance and analytics efforts: design and assess ML/deep learning solutions, conduct large-scale analyses, develop predictive models, advise leaders, collaborate with product teams, manage and coach a high-performing team, ensure AI trust, bias, ethics and risk-aligned decision-making across the enterprise.

Lead development of MRI-based machine learning models and end-to-end pipelines for probabilistic differential diagnosis in Parkinson's disease. Build clinician-facing decision-support tools, analyze large multimodal neuroimaging datasets, validate and calibrate predictive models, generate clinically interpretable outputs, publish and present findings, and mentor trainees while collaborating with multidisciplinary clinical and imaging teams.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

The Machine Learning Engineer II crafts and deploys advanced AI solutions leveraging machine learning technologies, focusing on real-world applications and scalable systems. Responsibilities include coding for model deployment, managing large datasets, and working within a collaborative AI team.

Lead execution of BI strategy, manage multidisciplinary teams, prioritize large BI initiatives, ensure quality deliverables, embed MLOps best practices, drive innovation and partnerships, and deliver analytics solutions for Product, Underwriting, and Pricing.

Use legal expertise to create and evaluate training data for AI models. Perform legal research and analysis, provide actionable feedback, follow annotation best practices, support senior staff, present findings to stakeholders, and advocate for responsible AI.

Build and own end-to-end ML features for an agent management platform in healthcare: design data models, build pipelines and backend services, integrate frontend when needed, ground and evaluate agent behavior using clinical data (FHIR/HL7), prototype with hospital partners, and create novel solutions for agent observability, evaluation, and continuous improvement.

Senior full-stack developer to design, build, and optimize cloud-native microservices using Angular, Spring Boot and AI/ML techniques. Troubleshoot, ensure security and performance, integrate systems, apply AI-driven code analysis, and mentor junior developers across the SDLC.

Perform end-to-end BI and data analytics work: extract and transform large datasets, build data warehouses and predictive models, create dashboards and reporting, manage client relationships, oversee analysts, and contribute to proposals and marketing as needed.

Lead data-driven business operations initiatives spanning customer acquisition, carrier and partnership growth, product development, and operational efficiency. Design, execute, and analyze A/B experiments; apply machine learning and complex data analysis to identify scalable growth opportunities; communicate insights to marketing, product, business development, frontline teams, and executives; and automate manual processes.

Drive data-informed decisions across growth, product, and business initiatives. Partner with marketing, product, and business development teams; analyze customer performance; design and evaluate A/B experiments; identify acquisition and partnership growth levers; and automate manual processes to improve operational efficiency. The role requires rigorous problem solving, advanced data analysis, SQL proficiency, and communication with both frontline teams and executives.

Drive data-informed decisions across carrier growth, product launches, user acquisition, and partnership initiatives. Partner with marketing, product, and business development teams; design and analyze A/B experiments; identify scalable growth levers; conduct complex analyses using machine learning models; and automate manual processes to improve operational efficiency. The role requires strong problem-solving, quantitative analysis, SQL, and communication skills.

Lead data-driven business operations initiatives across acquisition, partnerships, and new product development. Partner with marketing, product, and business development teams to analyze customer performance, design and evaluate A/B experiments, identify growth levers, and improve operational efficiency through automation. Use SQL and advanced machine learning models to generate insights that inform profitable, scalable business strategies and communicate recommendations to audiences from frontline staff to executives.

Lead the design, development, deployment, and maintenance of scalable cloud-native platform capabilities for Walmart Connect’s sponsored search ad server. Build infrastructure, tooling, AI/ML frameworks, and distributed systems; conduct design reviews; drive software lifecycle activities; mentor engineers; influence architecture and technical decisions; and collaborate across teams to translate complex business needs into reliable, data-driven solutions.

Own and evolve high-throughput backend services that deliver machine learning-powered predictions to merchants. Responsibilities include API design, service decomposition, Python development, PostgreSQL and Kafka integration, distributed systems reliability, observability, feature pipelines, ML serving, model deployment, versioning, rollback, and prediction monitoring. The engineer will partner with Data Science, lead architectural decisions and technical reviews, mentor teammates, and establish strong engineering practices for production data products.

Lead and deliver complex analytics, machine learning, and generative AI initiatives from problem framing to implementation. Design model strategies, evaluate AI/LLM solutions and agentic workflows, translate results into business recommendations, prepare business cases, mentor junior data scientists, champion Responsible AI and governance, and collaborate with product, engineering, and business partners to drive scaled AI outcomes.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.