Top Machine Learning Jobs in Toronto

Designs and leads enterprise AI and generative AI solutions from use-case definition through production. Responsibilities include cloud-native architecture using AWS Bedrock and SageMaker, defining AI standards, selecting platforms and models, partnering with executives, ensuring governance and compliance, and mentoring delivery teams. The role also incorporates responsible AI, risk management, monitoring, lifecycle practices, LLM platforms, RAG, agent systems, and MLOps.

ML Engineering Intern supporting real-time markerless motion capture systems. Responsibilities include deploying and monitoring pose estimation models in cloud infrastructure, curating training data, running model experiments, diagnosing failures, and improving evaluation and deployment tooling. The role requires 3D computer vision knowledge, Python, and deep learning familiarity, with opportunities to work on containers, orchestration, model optimization, and robotics middleware.

Develop and evaluate research approaches for 3D reconstruction, neural rendering, and spatial modeling. Implement techniques from research literature, run rigorous experiments, analyze results, and help transition promising models into production through profiling and optimization. The role focuses on classical and neural computer vision methods, including multi-view geometry, monocular depth estimation, NeRFs, and Gaussian splatting.

Support cloud infrastructure for machine learning training and deployment. Responsibilities include provisioning compute, orchestrating training jobs, improving MLOps workflows, assisting with model deployment and testing, investigating reliability, latency, and cost issues, collaborating with engineering teams, and documenting infrastructure tooling.

Leads a team of Technical Program Managers executing complex, multi-year technology initiatives, including cloud architecture updates, application convergence, integrations, and enterprise data migrations. Aligns Product, Engineering, Design, Data Science, and Risk stakeholders, resolves delivery bottlenecks, influences senior leaders, and establishes governance, metrics, release controls, and Agile operating practices. Coaches TPMs and improves delivery execution across a matrixed organization.

Lead technical strategy and pre-sales engagement for major financial services and insurance accounts. Guide enterprise customers through data and AI transformation, drive Databricks adoption, build technical champions, present solutions to executives, and coordinate sales and technical teams. Apply expertise in big data, data science, cloud platforms, machine learning, and production programming to deliver business outcomes across large global accounts.

Leads the technical strategy, architecture, and execution of enterprise AdTech platforms across front-end applications, data pipelines, ad integrations, analytics, and cloud infrastructure. Manages multiple engineering teams, oversees vendor partnerships, guides product and platform lifecycles, optimizes cloud costs, ensures privacy and data governance compliance, and integrates machine learning capabilities. Translates commercial objectives into scalable technical roadmaps while advising executives on AdTech trends, architecture, technical debt, and platform reliability.

Designs, builds, deploys, and supports production-grade AI applications for business stakeholders. Responsibilities include identifying AI opportunities, developing agents, Copilot solutions, automation workflows, and RAG systems; integrating enterprise data and applications; managing delivery through production; monitoring adoption and performance; and ensuring security, privacy, compliance, and responsible AI governance. The role also develops reusable engineering frameworks and advises leaders on AI strategy and implementation.

Design and operate large-scale data and ML platforms supporting audience data, feature stores, training pipelines, model serving, monitoring, and LLM-powered workflows. Own end-to-end data pipelines, reliability, SLAs, observability, and business impact. Apply distributed systems principles, production ML practices, and agentic technologies while collaborating across product and engineering teams in a fully remote environment.

Leads the end-to-end development and deployment of AI/ML models for enterprise financial reconciliation and data engineering. Designs Agentic AI and Generative AI solutions, analyzes structured and unstructured financial data, defines model roadmaps, and integrates models into production. Partners with engineering and business teams on scalability, risk mitigation, documentation, and adoption while translating complex findings into visualizations and strategic recommendations for senior leaders.

Designs and trains computer vision models for vehicle damage detection, builds high-throughput containerized model-serving microservices, and manages the end-to-end ML lifecycle. The role includes developing Kafka-based streaming data pipelines, optimizing models for real-time inference, maintaining annotation and evaluation frameworks, and collaborating with stakeholders to deliver scalable production solutions.

Leads the transformation of Sophos’s pricing operations by building a centralized function, establishing governance and segment-based pricing cadences, and developing AI-assisted competitive and discount analysis. The role builds a new team, partners with Network Security, provides leadership with market intelligence, and tracks pricing performance metrics. Candidates need experience transforming pricing, revenue operations, or deal desk functions in B2B SaaS or technology businesses, along with strong change-management and people-leadership skills.

Develops, deploys, and monitors production data and machine learning pipelines. Responsibilities include data preparation, exploratory analysis, predictive modeling, feature and algorithm selection, model deployment, infrastructure development, and monitoring model performance, data quality, drift, latency, and reliability. The role also requires communicating insights, collaborating cross-functionally, and translating business problems into scalable data and ML solutions.

Leads property and casualty insurance pricing analyses, rate development, predictive modeling, portfolio evaluation, and regulatory filing support. Develops and enhances GLMs, machine learning models, rating structures, and automated analytical processes using SQL, Python, R, Excel, and Earnix. Provides pricing recommendations, technical guidance, peer review, mentoring, and stakeholder communication while ensuring compliance with actuarial standards and regulatory requirements.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Own post-sale relationships with electric utility customers, drive adoption and ROI of the Buzz Solutions platform, identify expansion and at-risk accounts, coordinate training and enablement, advocate customer needs to product and engineering, and maintain account health data in Salesforce.

Lead the design and deployment of large-scale bioinformatics and omics pipelines spanning genomics, transcriptomics, and proteomics. Integrate multimodal biological data with machine learning, develop reproducible analysis frameworks, benchmark predictive models, and collaborate with wet-lab scientists to guide molecular engineering and experimental campaigns. Contribute novel computational biology algorithms, research roadmaps, and scientific leadership.

Lead the engineering organization for BlueCat’s Network Observability product line. Own technical strategy, architecture, roadmap, delivery, and operational excellence for data-intensive network monitoring platforms spanning SaaS and on-premise deployments. Build AI-driven observability capabilities, modernize legacy systems, establish engineering standards, and develop distributed teams. Partner with product, executive leadership, customers, sales, and support to achieve business goals and resolve strategic technical issues.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Leads Kinaxis’s Advanced Mathematical Modelling function, managing architects, algorithm developers, and operations research specialists. Provides technical and strategic leadership across optimization, machine learning, simulation, decision science, and algorithm development. Partners with Product Management, Engineering, and customer teams to solve supply chain challenges, guides research and prototyping, shapes platform architecture, and promotes scalable production solutions. Oversees talent acquisition, coaching, workforce planning, performance management, and career development while establishing the function’s vision and operating model.

Own post-sale relationships for electric utility customers, drive platform adoption, retention, ROI, and expansion opportunities. Build onboarding, health scoring, QBR, escalation, and customer success playbooks from scratch. Serve as a bridge between customers and internal teams, train customers, influence product priorities, and eventually lead hiring and development of the Customer Success team.

Leads an Investment Insights team creating analytics that improve investment manager effectiveness. Responsibilities include designing and developing investment analytics, researching methodologies, working with portfolio managers, productionizing solutions, applying AI, resolving complex analytical issues, and managing analysts across Canada and India. The role also shapes strategic priorities, develops team capabilities, mitigates process risks, and communicates technical concepts to varied stakeholders.

Develop investment analytics that improve investment manager effectiveness and decision-making. Research analytic methodologies, build proofs of concept, work with investment teams and leadership, and productionalize solutions with platform teams. Analyze financial datasets across performance, risk, attribution, liquidity, ESG, fees, and trading. Apply AI to financial modeling, anomaly detection, and natural-language data queries while resolving complex issues and communicating technical concepts clearly.

Design, prototype, and ship production AI/ML features that analyze observability data to detect, triage, and resolve incidents. Rapidly iterate on LLM- and agent-powered workflows, collaborate cross-functionally, integrate AI with internal tools and alerting systems, and ensure solutions are scalable, maintainable, and aligned with user workflows.