Top Python Jobs in Toronto

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

The Research Scientist will develop models for natural hazards, evaluate scientific literature, clean data, and implement projects using Python and GIS tools.

As a Reliability Engineer, you'll design, implement, and maintain AWS cloud environments, ensuring systems' reliability and performance, while enhancing monitoring and incident response capabilities.

Research AI threats, analyze threat actor behavior, produce actionable intelligence, collaborate with teams, and publish findings in the cybersecurity domain.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Responsible for managing wafer level testing processes to improve yield, overseeing test equipment, and coordinating with external suppliers and engineering teams.

Design and execute automated system- and solution-level tests (Python) for optical pluggable products, collaborate with cross-functional teams in Agile, maintain test scripts in Git, report test execution and defects, and drive continuous QA improvements and compatibility testing with third-party equipment.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Embed with customer teams running large-scale GPU training and inference to onboard, tune, debug, and improve reliability. Diagnose fabric, driver, scheduler, and application failures; profile performance; build automation, monitoring, and preflight checks; lead incident response; and convert field learnings into product improvements and reusable reference configurations.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Design and build end-to-end, enterprise-grade systems that enable AI agents to initiate and complete finance workflows. Own features across backend and frontend, integrate AI/ML models into auditable production systems, ensure scale, security, and reliability, and collaborate with product and customers to solve accounting problems.

Lead design and implement end-to-end features across frontend and backend for a data analytics platform. Build React/TypeScript UIs, backend services (JVM languages/Python/GraphQL), work with Postgres, cloud and distributed systems, collaborate with Product and UX, mentor engineers, improve processes, and adopt new technologies including AI to accelerate development.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Founding full-stack engineer to architect and build a production platform combining large-scale data systems and AI-native agents. Work across backend, frontend, data pipelines, agents, and deployment systems; define architecture, devops, CI/CD, and early engineering processes while shipping features and growing the team.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Lead rigging and CFX work including building, maintaining and troubleshooting body and facial rigs, cloth/hair/muscle/skin simulations, shot-level tech animation, tooling in Python/VEX, cache/version management with Alembic/USD, and mentoring junior artists while collaborating with animation, FX, lighting, pipeline and production.

Develop platform driver code, perform diagnostics testing, collaborate with hardware engineers, and support system deployment in AI datacenters.

The Senior Software Developer will lead design and implementation of backend systems, mentor peers, and enhance AI capabilities in tax research software, focusing on high-quality standards and rapid delivery.

As a Senior Machine Learning Engineer, you will design, develop, and deploy ML systems, mentor team members, and collaborate on advanced AI applications in tax research.

As a Research Intern, you will prototype and productionize research in AI and cryptographic schemes, collaborating with teams for real-world impact.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

Design business data models and data architectures, mentor data teams, translate business requirements into technical specifications, and set standards for AI-augmented development practices.