Top Machine Learning Jobs in Toronto

Own and deliver quarterly engineering goals for an ML feature platform, lead solutions to ambiguous backend and distributed-systems problems, and collaborate with product, design, and analytics teams. Maintain platform reliability through metrics, availability monitoring, and on-call support. Establish code quality and design standards, address technical and business issues, contribute to scalable system design, and mentor engineers while building highly available backend systems.

Own and evolve Dexterra’s software engineering practice by establishing standards, improving delivery quality, and aligning dispersed development teams. Lead enterprise platform modernization using microservices, API-first architecture, integrations, CI/CD, observability, and secure development practices. Translate business needs into architecture and delivery plans, support AI-enabled solutions, mentor engineers, and provide visibility into technical decisions, risks, dependencies, and progress.

Conduct applied research in quantum computing and quantum machine learning, designing experiments, proof-of-concepts, and algorithms for banking use cases such as fraud, cybersecurity, and optimization. Collaborate with AI researchers, engineers, and business stakeholders, translate theoretical findings into practical insights, communicate complex concepts to leadership, and identify opportunities for emerging quantum technologies.

Leads Cohere’s global Customer Success, Services, and Technical Support teams, driving adoption, customer experience, lifecycle strategy, and executive-level customer advocacy. Defines workflows and handoffs, coaches functional leaders, analyzes customer and support data, improves the customer journey, and influences product strategy. Partners with pre-sales, Product Management, and Machine Learning Engineering to represent customer needs and deliver value to enterprise AI customers.

Lead business consulting and delivery coordination for AI-enabled banking initiatives. Translate ambiguous problems into structured workstreams, requirements, roadmaps, and measurable outcomes. Assess AI opportunities, facilitate cross-functional decision-making, manage risks and dependencies, coordinate testing and adoption, and create executive-ready communications. Apply responsible AI, privacy, security, compliance, and governance practices while partnering with business, technology, data, risk, and control teams.

Own and grow strategic Anthropic and AWS alliances, building partner activation, funding, competency, co-sell, and pipeline programs. Lead the alliances team, establish operating processes, develop executive partner relationships, and collaborate with sales, marketing, and delivery. Expand partnerships with GCP and emerging AI providers while maintaining partner tiers and program requirements. The role requires hands-on alliance management, team leadership, enterprise communication, AI ecosystem familiarity, and up to 40% travel.

Develops AI and data strategies for client engagements, including vision statements, use-case identification, future-state capabilities, operating models, and implementation recommendations. Collaborates with consultants across regions and practices, translates technical concepts for business stakeholders, and supports data, analytics, machine learning, and AI solution delivery. Requires strong business acumen, critical thinking, communication, organization, and willingness to travel.

The Senior Data Scientist will collect and transform complex datasets, develop and deploy machine learning, NLP, generative AI, and agent solutions, and optimize models for accuracy, explainability, and ethical alignment. Responsibilities include building custom transformer models, monitoring production pipelines, communicating insights through visualizations and presentations, and collaborating with cross-functional teams to drive productivity, revenue, and strategic decision-making.

Leads an applications development team responsible for designing, implementing, and maintaining software systems. Oversees technical issue escalation, system analysis, application programming, project execution, resource planning, compliance, and risk management. Provides technical direction and coaching to developers while making decisions across backend and UI technologies. The role requires strong Java or modern UI development expertise, software engineering leadership, database knowledge, CI/CD experience, and familiarity with messaging, observability, cloud, and containerization technologies.

Lead the design, development, modernization, deployment, and support of scalable EOS software, including backend services, APIs, integrations, databases, data workflows, and Azure infrastructure. Establish architecture and engineering standards, improve reliability and observability, lead technical workstreams, mentor developers, coordinate vendors, support pre-sales, and apply AI-assisted engineering and automation. The role focuses on energy optimization platforms, distributed energy resources, electricity-market participation, and grid modernization.

Lead data science initiatives for agentic AI products, partnering with data scientists, AI/ML engineers, and product managers. Build, deploy, validate, and optimize machine learning models using large-scale numeric and textual data. Design A/B tests, analyze telemetry, translate technical findings into business strategy, research emerging technologies, and develop talent while guiding enterprise-scale AI decisions.

Own the full sales cycle for enterprise AI consulting engagements, from qualifying partner-referred leads through discovery, solution scoping, proposal development, negotiation, and signed SOWs. Engage banking, insurance, and life sciences buyers; collaborate with architects and delivery teams; manage partner relationships with AWS and Anthropic; maintain CRM accuracy and forecasts; and provide market feedback. Travel up to 30% for client meetings.

Develops and supports asset-liability management, hedging, valuation, reporting, and risk solutions. Responsibilities include analyzing financial data, designing workflows and integrations, prototyping AI agents and applications, validating AI outputs, troubleshooting systems, defining data models and controls, and collaborating with business and technology stakeholders. The role combines financial services expertise, business analysis, solution design, software development, SQL, programming, ETL, testing, documentation, and responsible AI practices.

Designs and evolves secure, governed, production-ready AI and generative AI architectures, including RAG, intelligent agents, vector search, and API integrations. Evaluates platforms, models, cloud services, and orchestration frameworks; defines security, privacy, performance, and operational requirements; guides MLOps and LLMOps practices; supports prototypes and MVPs; documents architecture decisions; and advises business and technology stakeholders within a regulated financial services environment.

Manage a high-volume portfolio of very small business customers and inbound prospects through the full sales cycle. Conduct discovery, demonstrations, proposals, negotiations, and closing while meeting revenue and activity targets. Identify expansion opportunities, maintain accurate Salesforce pipelines and forecasts, guide customers through adoption, and partner with Solutions Engineering, Customer Success, Legal, Onboarding, Product, Marketing, and Operations teams. Provide customer feedback and explain technical security concepts to nontechnical stakeholders.

Architect, develop, and maintain scalable Java automation frameworks for web interfaces, microservices, APIs, and databases. Build reusable Selenium, TestNG/JUnit, Cucumber, and REST Assured solutions; implement BDD/TDD and shift-left testing; integrate AI-assisted self-healing and predictive testing capabilities; and optimize parallelized CI/CD execution in Docker and Kubernetes. Diagnose automation, runtime, concurrency, and environment failures while collaborating with engineering teams to improve testability and resolve defects.

Lead and execute IT audits, assess IT risk and compliance (e.g., PCI DSS, PIPEDA), advise business and IT on controls, develop audit plans, report findings to senior management, and help integrate AI into audit practices while managing and mentoring the audit team.

Drives adoption and integration of AI technologies with the enterprise ERP system. Manages the AI requirements backlog, gathers and translates business needs into AI solutions, supports proof-of-concepts and implementations, leads post-implementation optimization, develops adoption metrics, trains teams, and evaluates emerging AI and automation tools. The role collaborates with business and technical stakeholders to deliver measurable value and improve software engineering efficiency.

Develop and improve machine learning and generative AI models for customer shopping missions, occasion discovery, transaction classification, and behavioral analytics. Research modeling approaches, design prompts, evaluate model quality, run experiments, analyze customer data, and establish testing frameworks. Partner with ML/AI engineers to productionize models, monitor performance, document decisions, and support personalization, marketing, merchandising, and customer intelligence use cases.

Develop and improve machine learning and generative AI models for customer shopping missions, occasion generation, and transaction classification. Research modeling approaches, engineer prompts, evaluate model quality, design experiments, analyze customer behavior, and monitor performance. Translate retail problems into modeling objectives, collaborate with ML engineers to productionize solutions, and communicate technical decisions and limitations to business partners.

Own analytical relationships with travel technology partners, create scalable reporting and dashboards, conduct exploratory analysis, apply machine learning and statistical methods, and translate findings into recommendations. Build automated and agentic workflows using LLM tools, collaborate with product and engineering, and present clear insights to commercial and executive audiences.

Lead machine learning infrastructure research for photonics-enabled AI systems. Build performance models for training and inference workloads, design rack-scale reference platforms, publish research and technical content, advise cross-functional teams, and lead a small team of machine learning engineers. The role requires deep expertise in AI systems, compute and networking technologies, performance evaluation, large language models, and academic research.

Develop and scale production machine learning models for credit decisioning, including default prediction and approval decisions. Build training, decisioning, monitoring systems, data pipelines, features, and algorithms. Conduct research, develop prototypes, and collaborate with engineering, credit, and product teams to deliver machine learning products from inception through business impact.

Provides L2 technical support for complex system issues, including performance bottlenecks, integration failures, database problems, and application issues. Troubleshoots Linux, MySQL, Java, networking, and system architectures; performs root-cause analysis; supports upgrades; documents solutions; improves knowledge bases; and trains L1 teams and customers. The role requires collaboration with R&D and product teams and may involve robotics, logistics automation, WMS, or WCS environments.

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