Top Machine Learning Jobs in Toronto

The Solutions Engineer supports the sales team by providing technical expertise, conducting demonstrations, designing solutions, and managing complex integrations during the sales cycle.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

As Research Manager at BeyondTrust, you'll conduct security research, develop detection methodologies, build security systems, optimize data analysis, and lead teams in cybersecurity innovations.

As a Product Manager at Arena Intelligence, you'll lead the evaluations platform, transforming AI research into trusted product infrastructure, while collaborating across functional areas and directly engaging with users.

Lead global payment operations at ABC Fitness, managing partnerships, optimizing processes, deploying AI solutions, and ensuring financial accuracy across operations.

As Staff Product Manager for the AI Platform, you'll define the vision and roadmap, establish execution methods, and collaborate across teams to ensure safe and efficient AI operations in a scalable enterprise platform.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.

As a Senior Product Manager for Flights Supply, you'll lead API integrations with airlines and third-party partners, focusing on automation, performance optimization, and user experience in the travel booking ecosystem.

Design and build data architectures, oversee ETL/ELT pipelines, collaborate with stakeholders to optimize data usage, and implement data quality frameworks.

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

As a Machine Learning Engineer, you will develop user experiences using LLMs and predictive analysis, deliver ML solutions, and ensure continuous improvement in AI products at scale.

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.

Lead and optimize cutting-edge AI models for custom hardware at Tenstorrent, collaborating across teams to enhance model performance and troubleshoot issues.

As a Senior Software Engineer, you will lead full stack development of AI-driven features, ensuring best practices in security and scalability while mentoring engineers and collaborating cross-functionally.

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

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.

The Principal AI Platform Engineer will design and maintain infrastructure for GenAI products, ensuring seamless integration and high performance. Responsibilities include implementing security controls, observability solutions, and optimizing scalability.

The Senior Data Scientist will analyze manufacturing data to improve quality and optimize processes through advanced analytics, with a focus on driving cost and waste reduction.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

As a Senior ML Engineer, you'll build ML infrastructure, ship AI features, define evaluation strategies, and enhance model decision-making in a dynamic startup environment.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

As a Senior Machine Learning Engineer, you will design and build AI systems, focusing on recommendation and ranking systems, while collaborating with cross-functional teams to enhance ML capabilities.

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