Top Python Jobs in Toronto

Leverage xAI's models and algorithms to create delightful consumer products and experiences, scale to millions of users worldwide.

Lead the design, development, and integration of innovative machine learning models in Firefox. Stay up-to-date with the latest advancements in machine learning and AI. Mentor and upskill the team on AI and generate thought leadership.

As a Senior Software Engineer on the Platform team, design, implement, and optimize cloud-based infrastructure and CI/CD pipelines on AWS. Work closely with product teams for efficient deployment, monitoring, and management of cloud applications.

Cockroach Labs is looking for a Software Engineer, Backend (Cloud Platform) to help manage and scale CockroachDB Cloud services and infrastructure across multiple cloud providers. Responsibilities include designing, building, and maintaining internal and customer-facing systems, developing software for increased reliability and operational efficiency, and troubleshooting mission-critical services. The role also involves working on automation, deployment of Kubernetes clusters, and participating in on-call rotations for production systems.

As a Field Engineer, you will guide the technical evaluation phase in a hands-on environment throughout the sales process. You will be a key part of the sales team, acting as a technical advisor and working with the product team as an advocate for your customers in the field. You will help our customers achieve tangible outcomes with Materialize, establish a clear architectural vision, identify compelling use cases, and help our customers realize value.

Software Development Engineer at Autodesk Research working on ML-powered product features, backend and frontend applications, data processing pipelines, 3D geometry rendering, bug resolution, code quality, and AI integration. Must have strong Python programming skills and experience in cloud infrastructure services.

Responsible for architecting, building, and designing a data collection platform, iterating on new features, building end-to-end features, monitoring data quality issues, establishing best practices, ensuring platform security and scalability, and innovating to develop AI systems.

Lead R&D for new and existing data science powered products, collaborate with engineering and product teams, provide technical expertise for non-technical stakeholders, educate colleagues on data science in marketing and advertising.