Top Machine Learning Jobs in Toronto

The role involves collaborating with customers to solve engineering challenges using machine learning, develop solutions, train teams, and improve products based on feedback.

The ML Platform Engineer enhances the ML platform's performance and scalability, supports machine learning solutions, and leads software development efforts.

The Staff Software Engineer will enhance the ML platform's performance and scalability, collaborating with teams to support predictive modeling and real-time inference capabilities.

You will build frameworks for LLM-powered agents, design reinforcement learning approaches, lead development of scalable evaluation systems, and mentor other engineers while writing robust code for enterprise-grade assistants.

Responsible for developing and deploying AI/ML models, integrating systems, optimizing LLM performance, and implementing MLOps practices within GCP.

The role involves improving machine learning ranking models, implementing algorithms, and taking full ownership of machine learning systems within Quora's product.

As a Senior Machine Learning Engineer, you will design, develop, and deploy ML systems, mentor team members, and collaborate on advanced AI applications in tax research.

Design and deploy machine learning systems for autonomous vehicles, collaborating with teams to improve model performance and safety using real-world data.

As a Senior Machine Learning Engineer, you will develop and optimize ML solutions, ensure efficient data flow, and lead project discussions with clients.

The role involves developing ML systems for enterprise workflows, focusing on user understanding, task prediction, and optimizing ML pipelines. Key responsibilities include designing scalable evaluation frameworks and collaborating with teams to deliver production-ready solutions.

Design and lead end-to-end AI/ML and GenAI architectures (data pipelines, training, inference, MLOps). Advise clients, integrate LLMs, build RAG systems, benchmark platforms, mentor teams, ensure responsible AI, and align ML strategy with business goals.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

The ML Engineer will develop and enhance machine learning models for credit decisioning, drive technical innovation, and collaborate across teams to deliver impactful solutions for small businesses.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.