Top Python Jobs in Toronto

Owns safety, risk, and compliance across robotics labs and data collection environments. Develops safety standards, SOPs, risk frameworks, audit programs, incident response, training, and compliance processes. Partners with engineering on safe robotic system design, influences leadership, reports risk trends, and may build and manage the safety function as operations scale.

Leads statistical programming for pharmaceutical clinical development programs. Develops and validates SAS or R programs, SDTM and ADaM datasets, tables, listings, figures, specifications, and regulatory submission deliverables. Oversees CRO programming work, supports clinical study reports and FDA submissions, maintains programming standards, contributes to CRF and validation planning, and collaborates with clinical, vendor, and regulatory teams.

Build and maintain scalable data pipeline architecture, process complex datasets, improve data delivery and infrastructure, and support data initiatives across engineering, product, business intelligence, and data science teams. Develop extraction, transformation, and loading infrastructure using Spark, EMR, Kafka, and other big data technologies while optimizing production-scale data and machine learning pipelines.

Develop and optimize high-performance features for SideFX’s 2D and 3D software products, including Houdini and Houdini Engine plug-ins. Maintain the glTF importer and exporter, support PDG, HAPI, games tools, and multiple hardware platforms, and debug product issues. The role requires modern C++ and Python development, strong 3D graphics knowledge, software quality and performance focus, independent problem-solving, and effective collaboration.

Build and operate security tooling across AWS infrastructure and codebases. Responsibilities include vulnerability scanning, CI/CD security integration, real-time threat detection using ELK and Grafana, AI-powered cloud perimeter monitoring, automated response, and production tooling lifecycle ownership. The role also partners with engineering teams to communicate findings and provide actionable remediation guidance.

Conduct quality assurance testing and risk reviews for US Operational Risk Management programs. Monitor first-line control activities, assess control effectiveness, identify process and control gaps, and provide recommendations. Enhance testing procedures, apply data analytics and automation, prepare executive reporting and workpapers, and communicate findings to senior stakeholders. The role operates in a hybrid arrangement with three onsite days per week.

Develop and improve front-end ASIC/SoC build methodologies, CAD flows, and automation from RTL development through implementation handoff. Build production-quality infrastructure for RTL quality, verification, IP management, regression, and release readiness. Collaborate with engineering teams and EDA vendors to resolve bottlenecks, evaluate AI and automation technologies, and drive methodology adoption across projects.

Manage software quality assurance across the product stack by creating UATs and test plans, triaging defects, maintaining CI/CD and staging systems, managing test automation, and supporting engineering teams. The role requires reading application and test code, troubleshooting pipeline failures, coordinating technical and non-technical stakeholders, and designing QA processes for SaaS and AI-powered applications.

Develop and maintain scalable automated testing solutions for complex applications and trading platforms. Responsibilities include UI, API, service-level, and database validation; test strategy and planning; CI/CD integration; continuous test execution; risk assessment; and collaboration with global business, development, and QA teams. The role also mentors junior engineers, advises stakeholders, supports application implementation, and independently manages quality engineering projects.

Manage enterprise cloud platforms centered on Kubernetes and Docker, including deployments, production support, monitoring, disaster recovery, security compliance, networking, storage, and automation. Develop CI/CD and infrastructure-as-code solutions, maintain documentation and administrator accounts, troubleshoot incidents, coordinate with development and support teams, and research new technologies. The role requires enterprise IT, cloud, DevOps, observability, scripting, service mesh, and container-management experience in a hybrid Toronto-based environment.

Lead cloud security direction across AWS, GCP, and self-managed Kubernetes. Define standards, reference architectures, IAM controls, threat-modeling practices, detections, incident-response playbooks, vulnerability-management workflows, and secure infrastructure-as-code and CI/CD practices. Serve as a senior incident responder and cloud-forensics lead, secure distributed systems and data stores, drive cross-functional initiatives, and mentor security and platform engineers as a senior individual contributor.

Consolidate and modernize PALU and CIMS data and deployment systems across Databricks, AWS, GitLab CI/CD, PostgreSQL, and dbt. Responsibilities include assessing Databricks, building data pipelines and models, configuring CI/CD, optimizing AWS costs, implementing containers and infrastructure as code, managing security and backups, monitoring systems, resolving maintenance issues, documenting configurations, and training MCD staff for independent system operation.

Develop and maintain distributed full-stack services across Angular and .NET. Design microservices using Domain-Driven Design, translate feature specifications into technical solutions, write unit and integration tests, review code, apply secure coding practices, and collaborate with Product and UX teams using Figma designs. The role also involves AI-assisted development, architecture contributions, REST APIs, SQL databases, and reliable deployment of customer-focused manufacturing software.

Design and implement Mojo language features, compiler optimizations, and multi-target compilation for CPUs, GPUs, and AI accelerators using MLIR and LLVM. Collaborate with kernel authors, hardware engineers, researchers, and the open-source community to improve language design, code generation, performance, developer experience, and the broader Mojo SDK.

Develop full-stack features for an AI-powered insurance application using Python, React, Redux, PostgreSQL, and AWS. Integrate AI models and APIs for document processing, data extraction, and recommendations; build scalable backend services and intuitive user interfaces; and contribute to architecture, code reviews, releases, and cross-team engineering initiatives.

Lead full-stack development of AI-powered applications using Python, React, Redux, PostgreSQL, and AWS. Define architecture for AI/ML systems, research emerging technologies, and ensure security, scalability, and observability. Partner with cross-functional teams on technical strategy, mentor engineers, support release planning, and resolve ambiguous system-wide challenges while delivering innovative production AI solutions.

Build full-stack features for an AI-powered insurance application using Python, React, Redux, PostgreSQL, and AWS. Integrate and deploy AI/ML models, develop data pipelines, design scalable cloud architectures, and create user experiences around intelligent functionality. Participate in architecture planning, code reviews, releases, cross-team collaboration, and engineering initiatives while researching new AI applications.

Design, build, and scale an agentic voice AI framework for banking customers. Develop LLM guardrails, grounding, policy enforcement, evaluation, tool-calling, and runtime architecture. Optimize real-time voice latency, maintain production reliability, reduce configuration friction, and collaborate with distributed product and engineering teams. The role also requires strong technical judgment, proactive ownership, AI-assisted development, and leadership through direction-setting, unblocking, and mentorship.

Design, build, and support production data feeds and integration pipelines using Python, SQL Server, Snowflake, REST APIs, SFTP, Docker, and AWS ECS. Develop incremental-loading workflows, apply Data Vault modeling, troubleshoot end-to-end data pipelines, and implement validation, monitoring, logging, error handling, and retries. Collaborate with technical and business stakeholders while maintaining pipeline and operational documentation.

Develop marketing budget allocation models, Early LTV metrics, performance diagnostic frameworks, and analytical tools. Analyze products, channels, campaigns, cohorts, attribution, and marketing economics using SQL, experimentation, BI tools, and spreadsheets. Support AI-based analytics development and unified BI metrics while translating findings into business rules and recommendations.

Research and implement macOS detection techniques and build them into production endpoint sensor features. Drive projects from concept through release, collaborate cross-functionally, troubleshoot issues, and apply secure, high-performance engineering practices using Swift/C++ and related tools.

Own the company’s technical SEO, answer engine optimization, and AI search visibility strategy. Build automated monitoring, AI agents, knowledge layers, structured content systems, dashboards, and technical search workflows. Partner with Engineering, Content, Product, Brand, and Analytics to improve discoverability, citations, organic demand, and revenue impact. Lead and develop the web team while establishing scalable AEO and technical search standards.

Own product features end to end, from discovery and technical design through deployment and iteration. Build across frontend applications, APIs, backend services, and integrations while contributing expertise in either frontend or backend engineering. Partner with Product, Design, QA, and Engineering to make scalable technical decisions, improve testing and delivery practices, and support production software. Mentor engineers through reviews, pairing, technical discussions, and knowledge sharing.

Architects, develops, and deploys high-performance machine learning systems across the full ML lifecycle. Responsibilities include building large-scale data and feature pipelines, training tree-based and deep learning models, developing low-latency inference services, implementing MLOps and monitoring, and designing rigorous experimentation. The role requires strong Python and software engineering expertise, distributed computing experience, production ML serving skills, and familiarity with statistical evaluation and A/B testing.

Supports investment analytics discovery, methodology research, data visualization, business intelligence, workflow improvements, data pipeline development, and AI applications. The intern will help modernize the analytics platform and develop scalable performance, attribution, risk, exposure, and modeling solutions while collaborating with project leads and investment affiliates.