Top Machine Learning Jobs in Toronto

Develop and commercialize firmware for neural network processing on ARM-based processors. Design embedded software for real-time processing, manage buffer interrupts, and optimize compilers.

Lead engineering teams to enhance retailer experiences, oversee project management, ensure software quality, and foster technical excellence.

Lead Scribd's product strategy for a new Discovery product organization, focusing on search and recommendations through advanced ML systems and user-centered design. Mentor PM teams, oversee the development of AI-driven features, and align product vision with business goals.

Lead the development of AI-driven security workflows, integrate machine learning models, establish data pipelines, and mentor engineers.

As an AI Research Scientist, you will lead projects that apply reinforcement learning strategies for engineering solutions and collaborate with a team to innovate in AI applied to the physical world.

As a Staff Data Scientist, you'll lead the analytical strategy for account transfers by building data products, driving operational efficiency, and mentoring team members to improve client experiences and retention.

The Director of Data Science will lead the data science team, develop predictive models, and enhance AI strategies to drive business growth.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

As a Software Engineering Intern, you'll develop features for user-facing products, work on API platforms, and design data pipelines while gaining hands-on experience in AI technologies.

Lead Machine Learning Engineer responsible for setting technical vision, guiding teams, architecting solutions, and overseeing project delivery on Google Cloud. Requires strong ML fundamentals and excellent communication skills.

Lead the design and optimization of programmatic bidding systems, collaborating with engineers and applying analytical methodologies to enhance performance.

The Data Scientist will solve real-world problems utilizing data science techniques and build end-to-end data pipelines, collaborating with multiple teams to deliver solutions.

The Data Scientist will analyze customer health, conduct quantitative research, develop insights for teams, and inform strategy based on data analysis.

The Technical Pre-Sales Consultant will engage with clients to understand their needs, recommend AI and data solutions, develop proposals, and deliver technical presentations, ensuring alignment of solutions with client requirements.

Lead the development of Authorization products, manage the full product lifecycle, drive innovation with external partners, and collaborate across teams.

The Technical Product Manager will oversee product strategies for complex initiatives in scheduling and forecasting, working with engineering and data teams to optimize and implement solutions.

Design and deliver scalable machine learning infrastructure and pipelines for AI/ML models, ensuring data integrity and collaborating across teams.

The AI Engineer role focuses on enhancing applied AI systems, developing tools for LLM applications, and optimizing workflows while collaborating with the product team.

As a Senior Data Scientist, you will detect fraud, optimize payments, analyze data via A/B testing, and collaborate with various teams to enhance Lime's payments system.

As an Applied ML Lead Scientist, you will develop predictive models, define AI strategies, oversee MLOps, and ensure governance while leading cross-functional teams to drive business outcomes.

Develop and launch algorithms for mapping services, collaborating with teams to apply ML for business and user impact, and writing scalable production code.

Develop and execute applied machine learning projects, mentor engineers, design features, and improve user ads action prediction for the ads conversion modeling team.

Develop and optimize data pipelines, integrate model pipelines, and build scalable AI/ML solutions, collaborating with data teams and stakeholders.

As a Machine Learning Intern, you will assist in developing, training, and deploying machine learning models, collaborating with data scientists.

The ML Engineer will build and deploy machine learning solutions at Autodesk, working with large datasets and collaborating to enhance customer experience. Responsibilities include model development, maintaining data pipelines, optimizing model performance, and integrating ML into products.