Top Machine Learning Jobs in Toronto

The Machine Learning Engineer will create scalable ML systems, build production data pipelines, and ensure robust deployment and monitoring of solutions.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

The Senior ML Performance Engineer will architect a Performance Testing Platform, optimize ML performance on GPUs, and validate model efficiency.

Develop and deploy models for the Piai claims-intelligence platform using machine learning, NLP techniques, and collaboration with various teams.

Manage and lead a team of engineers, oversee software development for ML platforms, and ensure high-quality project execution while collaborating across teams.

As an AI/ML DevOps Engineer, you will operationalize AI solutions, build CI/CD pipelines, and ensure production system reliability. Collaborate with data scientists and engineers to deliver AI capabilities and maintain strong operational practices.

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.

You will build a search and assistant product, improve personalization, train models, and write robust code while mentoring junior engineers.

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

Lead scaling and innovation of ML systems: design and implement models for medical/insurance documents, scale inference and training pipelines, build robust Python APIs, ensure reliability and model lifecycle management, collaborate across teams, and develop evaluation with human-in-the-loop processes.

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

As a Principal Software Engineer II, you will build and maintain machine learning components for Elasticsearch, optimizing model performance and collaborating with various engineering teams on advanced analytics.

The Databricks ML Administrator will manage ML environments, oversee governance, secure operations, and ensure reliable model training and deployment.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

As a Machine Learning Engineer, develop ML models, data pipelines, conduct analytics, and work on image and video inference for fish health and behavior.

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

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.

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

Lead the engineering vision and execution for the Customer & Business Insights Platform, focusing on data architecture, pipelines, and product delivery while mentoring global teams and ensuring data governance.

Lead technical direction and execution of CX automation systems to reduce support costs and speed resolution. Architect scalable backend services, decision engines, and workflows that take real actions with fraud/abuse guardrails. Partner with Product and CX Operations, drive platform investments, and measure automation effectiveness.

Design and build agentic AI workflows for biological and clinical data curation and analysis. Implement context engineering (RAG variants), prompt and agent architecture optimization, LLM tool-use integrations, and evaluation benchmarks. Collaborate with bioinformatics and clinical experts to translate domain problems into reliable, production-quality agentic systems.

Lead design and deployment of production-grade AI solutions, build multi-agent systems and cloud-native ML infrastructure, apply rigorous statistical evaluation, implement generative and predictive models, champion MLOps and data pipeline best practices, and collaborate with clients to translate business needs into technical solutions.

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 developing high-performance AI features for observability, rapid prototyping, and cross-functional collaboration, aimed at improving incident response and system behavior understanding.