Top Machine Learning Jobs in Toronto

Lead end-to-end delivery of large-scale, technically complex customer programs for Phaidra's industrial AI control systems. Own program strategy, customer relationships, cross-functional coordination, risk mitigation, documentation, and continuous process improvements while mentoring TPMs and aligning product and engineering to customer needs.

Define technical vision and roadmap for the company ML platform. Architect scalable ML pipelines, MLOps practices, security, observability, and integrations. Guide model training, deployment, serving, monitoring, and provide technical leadership and mentorship across engineering teams.

Own the end-to-end machine learning lifecycle, including dataset creation, research, rapid experimentation, model development, deployment, monitoring, and optimization. Build production-grade ML and LLM solutions, apply techniques such as quantization and pruning, track experiments systematically, and maintain scalable MLOps pipelines. The role requires strong Python and PyTorch expertise, production ML experience, and hands-on knowledge of LLM fine-tuning, prompt engineering, RAG, and efficient inference.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

The Machine Learning Scientist will lead open-source research, design experiments, develop novel methodologies, and analyze large-scale data to advance AI model evaluation and transparency.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

Lead computer vision projects and develop ML models for power grid analysis while mentoring engineers and ensuring software quality standards.

The Machine Learning Engineer will design, develop, and deploy ML pipelines, optimize models, collaborate on data workflows, and implement MLOps best practices in a fintech environment.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The role involves designing scalable AI solutions, managing large-scale AI deployments, building client relationships, and delivering presentations. Requires experience in MLOps and knowledge of ML ecosystems.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

Lead the design and development of software for quantum machine learning methods. Collaborate with researchers and communicate findings. Oversee best practices in machine learning systems and influence product offerings.

Develop AI-powered features, design APIs, integrate AI/ML models, and enhance developer experience for ClickHouse's platform.

Build and maintain full-stack marketplace features and support ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring) on Chalk and Vertex AI. Collaborate with data scientists to productionize models, write well-tested code, address performance/scalability issues, and contribute to technical planning.

Build and maintain backend and full-stack marketplace features while supporting ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring). Collaborate with data scientists to bring models to production, write well-tested code, address performance and scalability issues, and contribute to technical planning and cross-functional communication.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Design, build, and deploy predictive and generative ML models (including LLMs) for banking use cases. Create production-ready ML pipelines, perform evaluation/A-B testing and monitoring, translate business problems into ML solutions, and communicate results to stakeholders while ensuring code quality and model robustness.

Develop and maintain simulation scenes, implement motion-planning and collision-checking in ROS2/MoveIt2, build perception components for detection and point-cloud processing, write tested C++/Python code, debug software-hardware integration, and participate in code reviews, CI, and agile practices in onsite R&D/production facilities.

Develop and deploy deep learning models across vision, language, audio, and multimodal domains; build end-to-end workflows from data curation to on-device or cloud deployment; prototype and integrate generative and foundation-model research into product features; collaborate cross-functionally and publish or patent innovations.

Lead the design and deployment of AI/ML solutions for grid automation, manage teams, drive innovation, and ensure adherence to architectural standards.