Top Machine Learning Jobs in Ottawa

Design, build, and scale ML systems for demand forecasting and vehicle positioning. Collaborate with teams and mentor engineers to enhance ML expertise.

The role involves designing, building, and maintaining cloud infrastructure for AI/ML workloads, collaborating with stakeholders, and developing AI solutions using various technologies.

Lead the Safety ML team as a technical lead, driving ML systems development for safety, collaborating with cross-functional teams, and tackling complex safety challenges.

Develop AI solutions and enhance observability data using AI-powered features. Collaborate cross-functionally, iterate rapidly, and take ownership of AI projects while ensuring scalability and impact.

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 Machine Learning Engineer will lead machine learning projects to enhance home decor visualization, collaborating with teams to refine algorithms and user experience.

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.

As a Senior Machine Learning Engineer specializing in Reinforcement Learning, you will develop and deploy RL solutions, optimize ML models, and lead client discussions while ensuring high-quality engineering practices are followed.

Lead technical initiatives in ML product development focusing on LLM and NLP systems. Mentor engineers, design models, and oversee data strategies while ensuring operational excellence and model performance.

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

The Senior Machine Learning Ops Engineer will design and build machine learning operations infrastructure, manage ML pipelines, experiment tracking, model deployment, and collaborate with engineering teams to enhance data and ML processes.

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

The ML Solutions Architect is responsible for leading client technical discussions, designing scalable ML solutions, and ensuring project success by bridging clients and delivery teams.

The Senior AI Engineer will develop AI features for observability tools, collaborate with cross-functional teams, and deliver scalable AI solutions.

Design and optimize machine learning pipelines for large-scale systems, integrating models into user features and ensuring data integrity and performance.

Lead the design and optimization of ML systems at Scribd, managing cross-team projects, mentoring engineers, and shaping technical direction for ML initiatives.

Design and optimize machine learning systems, build scalable data pipelines, and run experiments to improve recommendations for Scribd's products.

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.

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

As a Senior Fullstack Software Engineer, you'll operationalize ML models, collaborate with data scientists, maintain CI/CD pipelines, and improve ML infrastructure and frontend integrations.

Design, build, and deploy scalable ML solutions using GCP and Vertex AI. Collaborate with teams to operationalize models and monitor performance.

The role involves building and maintaining an MLOps platform, optimizing machine learning models for security data, and collaborating with teams to enhance data processing systems.

The Machine Learning Engineer II will develop and maintain AI/ML systems, manage cloud operations, and collaborate across teams to deploy ML-powered tools.

Lead an AI/ML/Data Science team to develop and deploy machine learning solutions for robotics, focusing on innovative capabilities and cost-effective cloud processing pipelines.