Top Python Jobs in Toronto

Lead system ownership and rebuild of the Core Lending Platform: define business rules, translate regulatory and business requirements into prioritized epics, ensure accurate loan economics, partner with Finance/Operations/Risk/Data teams, map and re-engineer processes, document algorithms and data, and deliver audit-ready reporting and reliable servicing capabilities.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Lead post-silicon bring-up and optimization of wafer-scale engines: develop debug flows, HW-SW codesign optimizations, build large-scale test infrastructure, create self-checking metrics and instrumentation, and collaborate across silicon, performance, and software teams to improve production performance and release processes.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Lead post-silicon bring-up and performance optimizations for Cerebras Wafer Scale Engines. Develop/debug production-ready flows, instrumentation, and self-checking metrics. Build infrastructure for large-scale silicon workload testing, collaborate with silicon architects, performance and software teams, and streamline CI/CD and release workflows.

Design, build, and maintain scalable backend and frontend automation solutions for analytics and reporting. Partner with stakeholders to streamline processes, create and optimize data assets and dashboards, maintain SQL databases (cloud and on-prem), measure automation ROI, support projects, and mentor junior team members.

Support client operations through data collection, cleansing, transformation, modeling and reporting. Build and automate standardized reports, design surveys and market tests, use BI tools to visualize insights, and make data-driven recommendations to support sales and marketing objectives.

Design, build, test, deploy, and maintain scalable batch and streaming data pipelines and services processing high-volume EDI transactions. Participate in full SDLC, collaborate with analysts and product teams on analytics use cases, improve observability and performance, support production, and work in an agile, distributed team using modern DevOps practices.

Design, deploy, and maintain highly available container orchestration platforms and deployment automation. Build monitoring/observability, automate failure handling, integrate AI agent orchestration and AI coding tools, collaborate with product and service teams, write and test code, and provide documentation, capacity planning, and reliability improvements.

As an AI Product Engineer at Puzzle Cats, you will develop and enhance AI-driven mobile game experiences, collaborating across teams to rapidly prototype, ship, and iterate on features that can quickly reach millions of players.

Design, build, and optimize scalable ETL/ELT data pipelines and analytical datasets using Snowflake and Qlik. Own end-to-end delivery from requirements to support, implement data modeling and performance/cost optimizations, automate ingestion and transformations, monitor and troubleshoot pipelines and reports, and collaborate with claims, analytics, and actuarial teams to translate business needs into robust data solutions while supporting migration and modernization efforts.

Lead commercial P&C pricing and predictive analytics efforts: maintain and enhance SAS-based pricing tools, collaborate with Data Science and underwriting to develop and validate models, deliver data-driven pricing recommendations, support small business pricing, mentor junior analysts, and drive process improvements and automation.

Analyze and transform claims and financial data to deliver insights, automate data-science workflows, identify opportunities to improve claims payment accuracy and reduce expense, benchmark performance, and collaborate with Claims, Actuarial, Pricing, Data Science, Underwriting and Risk to influence operational improvements.

Lead end-to-end improvements to pricing ML systems: analyze market and vehicle data, design features, build/validate/deploy production ML models, run experiments, and partner cross-functionally to improve margin and conversion. Expand work into lending, fraud, logistics, search, and recommendations as needed.

Design, build, and optimize Databricks-based data and ML platforms. Own ETL pipelines, Delta Lake lakehouse, MLflow model lifecycle, MLOps, cluster optimization, RAG/LLM solutions, BI integration, and data governance with Unity Catalog.

Build and maintain platform services for user lifecycle and HRIS integrations that process millions of records. Design, implement, and operate scalable, reliable microservices, APIs, and event-driven systems using cloud and container technologies. Contribute to design and code reviews, troubleshooting, migration from monoliths, estimation, and mentoring junior engineers.

Design, develop, and maintain backend microservices for reporting, analytics, data export and data-driven features. Collaborate with product, data, and engineering teams, implement APIs for BI tools, ensure data quality/governance, participate in design reviews, mentor junior engineers, and support agile delivery.

Lead architecture and implementation of scalable analytics, reporting, and AI-driven data intelligence capabilities. Own end-to-end services, drive technical direction, mentor engineers, ensure operational excellence, and collaborate with product and design to deliver secure, high-performance, maintainable solutions using cloud-native and data-platform technologies.

Lead end-to-end development of scalable platform features, drive system design and architecture, mentor engineers, champion best practices and tooling, collaborate with Product and Design, and solve complex distributed systems challenges using modern technologies.

Lead design, build, and maintenance of User Domain and HRIS integration services handling millions of user records. Architect scalable, secure microservices, mentor engineers, own features end-to-end, collaborate cross-functionally, and improve performance, reliability, and tooling.

The role involves designing and tuning control systems for robotics, integrating hardware, developing control algorithms, and collaborating with engineers to enhance robot motion and responsiveness.

Lead development and execution of payment fraud strategies. Investigate and monitor fraudulent activity, perform SQL-driven analytics and root-cause reporting, build and deploy fraud detection models, and collaborate with product, engineering, and vendors to implement automated fraud controls.

Design, prototype, and productionize machine learning models for real-world problems using structured and unstructured data. Implement scalable ML systems, apply statistical methods, rapidly prototype research papers, and collaborate with engineering teams to deliver data-driven solutions.

Lead data science engagements: partner with client executives, identify high-impact projects, design end-to-end data pipelines and models, build KPIs and production dashboards, mentor teams, and deliver scalable data products driving business impact.

The role involves preparing financial reports, ensuring compliance with regulations, participating in financial transformation projects, and supporting automation and process improvements.