Top Python Jobs in Toronto

Lead architecture and evolution of scalable, fault-tolerant backend systems powering real-time AI agents and conversation intelligence. Design data storage and retrieval, partner with ML engineers to operationalize AI, drive production performance, cost efficiency, observability, and mentor senior engineers while owning production reliability.

The Senior Staff Physical Verification Engineer leads all physical verification activities including DRC, LVS, and DFM for advanced chip designs, ensuring compliance across multiple foundries and technologies.

As a Senior Robotics Software Engineer, you will maintain and improve the Tally software stack, develop new software packages, enhance autonomy and navigation, and assist in DevOps and CI/CD processes.

As a Software Engineer, you will develop and maintain the Tally software stack, enhance robot navigation and autonomy, and support CI/CD processes.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

The Data Operations Specialist will manage data operations, ensuring quality data services and compliance, while optimizing data workflows and conducting audits.

Join Citi's Prime Analytics team as a Python Developer, collaborating with market quants to enhance analytics offerings using Python and related technologies, ensuring integration and compliance while delivering innovative solutions.

Own model selection, evaluation, and deployment for voice and multimodal restaurant AI agents. Build datasets, experiments, and monitoring to improve accuracy, safety, latency, and cost. Partner with product and engineering to ship model changes with guardrails, rollouts, and clear metrics tied to business outcomes.

Build and operate scalable, secure cloud infrastructure for real-time AI services. Improve reliability with SLOs, observability, capacity planning, and failure testing. Implement IaC, CI/CD, deployment safety, secrets and vulnerability management. Partner with product and AI teams, diagnose distributed failures, reduce cost, and automate platform patterns to enable faster, safer releases.

As a Senior Software Engineer, you will develop AI-powered systems to enhance accounting workflows, build backend services, and ensure optimal performance and reliability of AI solutions.

Build end-to-end AI-driven restaurant products from discovery through production. Own full-stack interfaces, backend services, agent workflows, integrations (POS, reservations, telephony), evaluation systems, and instrumentation. Design agent workflows combining models, rules, memory, and human escalation. Work directly with product, design, and restaurant users to iterate, improve reliability, latency, accuracy, and cost, and ship production-quality solutions.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

Design and architect front-end network fabrics for AI/ML and HPC clusters. Automate deployment of network infrastructure, debug networking issues, and lead technical projects across teams.

Design, implement, optimize, and validate high-performance ML and linear algebra kernels for Cerebras hardware. Develop low-level assembly and CSL routines, measure and tune performance, build testing methodologies, and collaborate with chip and system architects to maximize compute utilization for AI and HPC workloads.

Join the Kernel Reliability team to improve the reliability of compute clusters, assist in debugging, and enhance system tools alongside hardware teams.

Join the Inference Core Model Bringup team to bring up ML models on Cerebras CSX systems, focusing on performance, optimization, and debugging.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Seeking a Senior Data Scientist to lead data science initiatives, drive machine learning strategies, implement data solutions, and collaborate with cross-functional teams to enhance programmatic efficiency.

The DevOps Engineer will onboard applications, set up automated build and testing servers, and maintain non-production environments while promoting DevOps principles.

The role involves onboarding applications, setting up automated build and testing environments, and advocating for DevOps processes. Requires scripting proficiency.

Implement and manage DevSecOps practices in SDLC, focusing on security integration, CI/CD pipeline hardening, cloud infrastructure security, and compliance documentation.

The AI Developer will build and maintain data pipelines, design RAG systems, develop AI workflows, and ensure production reliability in healthcare settings.

Design and develop AI tools for smart grid applications, integrate ML models into software, and optimize system performance.

Develop and deploy deep learning models across vision, language, audio, and multimodal domains; build end-to-end workflows from data curation to on-device or cloud deployment; prototype and integrate generative and foundation-model research into product features; collaborate cross-functionally and publish or patent innovations.