Top Python Jobs in Toronto

The Information Systems Engineer will design integrations and enhance engineering processes across various business systems, focusing on scalability and efficiency. Responsibilities include collaborating with teams to implement systems solutions, optimizing integrations, monitoring performance, and supporting production applications.

Owner of end-to-end revenue and first-order analytics: define stakeholder questions, build datasets and dbt/Snowflake models, run SQL/Python analyses, deliver dashboards or AI-driven tools, and translate results into actionable sales and go-to-market recommendations while maintaining data quality and documentation.

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

The Senior Python Engineer will develop and support MongoDB Python drivers, collaborate with teams, and resolve bugs, enhancing user experience with MongoDB tools.

Lead backend engineering for AI-powered, real-time in-vehicle experiences. Define architecture and APIs for edge-constrained clients, build orchestration for concurrent agent/voice interactions, ensure reliability, observability, and safety of AI systems, mentor firmware engineers, and ship zero-to-one products.

Lead design, automation, and maintenance of cloud-based database infrastructure (primarily SQL Server and MySQL). Improve reliability with monitoring, HA/DR, automation, troubleshooting, on-call support, and mentoring of junior engineers while collaborating across teams.

Build and operate ML training and serving infrastructure: design, develop, and launch backend systems, support availability and on-call, collaborate cross-functionally, and improve developer velocity.

Lead technical strategy and architecture for Coinbase Payments: design and evolve high‑availability, low‑latency funds‑flow and rails systems, drive cross‑team initiatives and reliability improvements, enforce financial correctness, mentor senior engineers, and align stakeholders across product, risk, and compliance.

Lead design and implementation of Affirm's Lakehouse Platform (Iceberg, Spark, Snowflake). Define technical strategy, enable secure governed access, improve analytics engineering practices, optimize performance and cost, operate at scale, mentor engineers, and collaborate cross-functionally to deliver reliable, discoverable, and cost-efficient analytical data infrastructure.

Develop, launch, and maintain backend systems for card issuing and transaction processing. Break down projects, collaborate with product/design/analytics, write clear well-tested code, support operations and on-call duties, create/monitor metrics, perform code reviews, and participate in hiring and team growth activities.

Lead design and delivery of full-stack, AI-driven products and platform capabilities. Own technical strategy, ship end-to-end features, build scalable data and API systems, mentor engineers, and partner with product and customer teams to turn ambiguous real-world problems into measurable outcomes.

Build and own technically complex, mission-critical full-stack systems and AI-powered products. Define technical strategy, design and ship end-to-end features across backend, frontend, and mobile, and scale data ingestion and APIs. Lead cross-team projects, mentor engineers, drive technical standards, and partner with product, design, and customer teams to deliver measurable impact.

Build and productionize statistical and ML models for Toast's restaurant platform. Partner with engineers, product, and business teams to mine data, design experiments, evaluate models, and communicate actionable insights that drive product growth and customer outcomes.

Design, implement, and maintain scalable backend systems for campaign creation, targeting, content assembly, scheduling, and handoff to delivery. Ensure reliability, observability, and operational excellence; own features end-to-end, mentor junior engineers, and drive extensible, fault-tolerant distributed architectures.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Build and own core components of an LLM-powered agent platform: orchestration, tool integrations, APIs, knowledge/memory infrastructure, trust & safety, and evaluation tooling. Partner with Operations to translate workflows into agent capabilities, mentor teammates, and ship production-ready, scalable AI-native systems.

Partner with Talent Acquisition and People teams to design and deliver People-platform integrations, data services, and AI-enabled automations. Build reusable integrations and platform patterns (Workday, Greenhouse), evaluate vendors, prototype AI solutions, and steward platform operations, governance, and security to scale People technology across the organization.

Build reliable AI-powered, agentic backend systems to remove friction across the software development lifecycle. Design multi-step orchestration, safety checks, and evaluation tooling; apply distributed-systems judgment; work across GitLab codebases and with a distributed team to productionize solutions that may become customer-facing.

Lead technical direction for a greenfield team building agentic/LLM-backed systems to reduce SDLC friction. Drive discovery through shipping, apply distributed-systems judgment at scale, mentor engineers, own technical hiring and cross-team alignment, and evaluate correctness and safety of agentic workflows.

Build and iterate underwriting ML models for real-time transaction decisions. Develop feature pipelines and training datasets, prototype and evaluate models, productionize into batch or real-time systems, and instrument model/data health, retraining, and monitoring. Collaborate with engineering, risk, product, and ML platform teams to deploy robust, low-latency solutions with appropriate risk controls.

Lead the FinOps engineering team to design and implement backend services, cost-optimization tooling, data pipelines, APIs and dashboards, mentor engineers, and partner across engineering, finance, and product to drive cloud and vendor cost efficiency.

Lead end-to-end data science initiatives: design, deploy, and monitor ML/statistical models for credit risk, pricing, collections and fraud. Collaborate cross-functionally to integrate models into production, define model development standards, mentor team members, and maintain scalable ML pipelines and monitoring systems to drive measurable business impact.

The Staff Software Engineer will shape technical direction, build developer platforms, and design AI agents, enhancing developer productivity and collaboration across teams.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, forecasting, targeting, and personalization. Collaborate with engineers and product teams, set modeling and productionization standards, mentor data scientists, guide architecture and experimentation, and apply cloud and MLOps best practices to drive measurable product and business impact.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.