Top Machine Learning Jobs in Toronto

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.

Lead creation of CRA-compliant SR&ED technical narratives for software R&D. Conduct developer interviews, synthesize complex software/ML/cloud/embedded work into audit-ready reports, collaborate with PMs and finance, manage multiple claim deadlines, and maintain SR&ED compliance and best practices.

OneSix is seeking motivated professionals for future opportunities in data and AI consultancy. Interested candidates should submit their resumes to stay in touch.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The Account Executive will drive sales, develop strategies for business growth, manage accounts, and lead collaborations with clients and teams in the insurance sector.

The Hyperscalers and Commercial Lead will develop technology-driven business strategies, drive customer adoption, and manage end-to-end sales processes while working closely with cross-functional teams.

Partner with multinational clients to gather requirements, define KPIs, develop business cases, and oversee GRAVTY® loyalty program implementations. Provide product expertise, deliver client training, manage project documentation and SLAs, collaborate with product management on features, and create product marketing collaterals to support go-to-market strategies.

Lead pre-sales efforts for Media & Entertainment customers: present Snowflake to executives and technical audiences, deliver demos and POCs, design solutions, collaborate with product and engineering, and close large deals while maintaining deep industry and competitive knowledge.

Interns will prototype rapidly, iterate, and contribute to open-source projects at the intersection of AI and cryptography.

The Head of AI will define and execute an AI roadmap, lead research initiatives, mentor the AI team, and collaborate with senior leadership to enhance health technology.

Lead a major product area end-to-end: set strategy and KPIs, define customer personas and market thesis, prioritize features, drive execution with engineering, and align cross-functional stakeholders to deliver high-impact AI-enabled hiring and people-analytics solutions.

Lead Narvar's Promise team of frontend, backend, and data science engineers to deliver AI-driven delivery date predictions. Drive roadmap, set technical direction, maintain quality, hire and grow staff, run hypothesis-driven cycles, and collaborate cross-functionally. Stay hands-on with AI/ML, cloud infrastructure, and engineering practices to improve SDLC and product conversion.

Identify business challenges and translate them into AI initiatives; architect AI solutions using appropriate models, frameworks, and infrastructure; guide data science and engineering teams through proof-of-concept development and deployment; mitigate bias and model drift through monitoring; and communicate an AI vision aligned with business goals.

Lead end-to-end performance creative: ideate, script, produce, and edit ads for paid social, CTV, and emerging channels. Translate performance data into creative direction, manage agency and production partners, and leverage AI tools to accelerate production while raising visual quality and brand standards.

Lead data-driven initiatives by partnering with marketing, product, and BD to inform acquisition and partnership strategies. Design, run, and analyze A/B experiments, leverage machine learning for in-depth analyses, test growth levers, and automate manual processes to improve operational efficiency.

Lead enterprise sales for AI-powered SaaS solutions, driving new logos and expansion. Manage complex 6–12 month sales cycles and high six-figure deals, multi-thread stakeholders, build pipeline, translate technical AI capabilities into business outcomes, coordinate cross-functional teams, and track opportunities in Salesforce.

Support business growth by driving data-driven decisions: run analyses, design and analyze A/B tests, define acquisition levers, partner cross-functionally, and automate operational processes.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Partner with cross-functional teams to drive data-driven growth: design and analyze A/B tests, leverage machine learning for insights, define acquisition and partnership levers, and automate processes to improve operational efficiency.

Partner with marketing, product, and biz-dev to drive data-driven growth. Design, run, and analyze A/B tests, define acquisition and partnership levers, leverage machine learning for insights, and automate operational processes to improve efficiency and scalability.