Top Machine Learning Jobs in Toronto

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.

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

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.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

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

As an Applied Machine Learning Platform Engineer, you'll design and maintain training infrastructure, manage distributed pipelines, and optimize data workflows for machine learning models.

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

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.

Design, build, and deploy scalable ML platforms and pipelines; optimize and monitor model performance; enforce model governance and data compliance; mentor junior engineers; integrate AI solutions with product and engineering teams to deliver measurable business outcomes.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

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

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

As an intern, you'll develop software and machine learning systems for robots, focusing on perception, intelligence, behavior, and real-time software integration.

The ML Engineer will develop and advance multimodal models for robot perception and behavior, integrating these systems into hardware while collaborating closely with robotics engineers.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Lead finance analytics, own data warehouse and BI strategy, build Finance AI automation and model foundation, optimize GCP costs, create self-service analytics, ensure data governance and audit readiness, and manage a cross-functional team supporting FP&A and accounting.

Lead and scale a product-focused data science organization to build production ML systems for personalization, recommendation, experimentation, and causal inference. Define strategy, partner with Product and Engineering, own end-to-end ML lifecycle, mentor the team, and measure product impact through robust analytics and monitoring.

Design, build, and own production ML and LLM-driven features (RAG, prompt engineering, fine-tuning, semantic search). Write maintainable code, create design docs, troubleshoot issues, collaborate with product/design, and participate in Agile development. Work with Python/Java/JavaScript, ML frameworks, and cloud/container technologies.

As a Principal Software Engineer, you will lead the architectural evolution of identity systems, manage IAM services, and collaborate across teams to ensure secure, scalable applications at Kaseya.

As a Machine Learning Ops Engineer, you'll develop robust code, build and maintain MLOps infrastructure, QA systems, and mentor junior engineers to enhance AI model performance for mining clients.