Top Python Jobs in Toronto

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Provide fourth-level IP network support and debugging for escalated customer issues, coordinate with R&D and support teams, perform on-site adaptations, maintain problem records and release notes, and support product reliability and test environments across global teams.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

Serve as primary customer contact for strategic accounts, driving onboarding, technical integrations, and analyses. Provide product education, conduct API and timeseries analyses, develop BI tools, research incentives and regulations, and feedback to Product and Engineering. Support Sales and Marketing with battery storage domain expertise to maximize customer value and retention.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

Design, administer, and optimize Postgres databases on cloud-native platforms. Triage and remediate production database incidents, manage replication/backups, evaluate schemas and queries for performance, and build repeatable DB architecture using IaC and GitOps for AWS/GCP Kubernetes environments.

Bridge frontier multimodal research and product by shaping research priorities, interpreting evaluations, building prototypes, and defining research-to-product handoffs across Canvas, Agents, and the model platform to deliver enterprise-facing features.

Work across modeling, data, systems, and evaluation to make video foundation models expressive, controllable, and personalized. Build adaptation and personalization pipelines, define end-user quality metrics and evaluations, and collaborate with product and design to deploy production-ready model variants for creative partners.

Build and ship production AI features (LLM agents, tool/function calling, RAG pipelines) and reliable backend services. Implement observability, tests, rollback/fallbacks, and evaluation to keep models grounded, safe, and cost-effective. Collaborate with product, design, and GTM; run experiments and raise engineering standards.

Own end-to-end technical relationships for enterprise customers: lead onboarding and complex implementations across cloud data warehouses, drive adoption and expansion, deliver executive QBRs, build AI-powered automations and internal tooling, and enable agentic AI workflows to reduce engineering dependency and accelerate marketing outcomes.

Lead technical strategy and hands-on implementation for Faire's data platform. Architect and scale ETL/ELT, streaming and batch pipelines, data warehousing, orchestration, monitoring, cost optimization, security, and governance. Mentor teams, prioritize platform roadmaps, and partner with product, analytics, and data science to enable self-serve data capabilities across the company.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

The Motion Planning & Controls Engineer will develop software for robot motion, implementing algorithms for optimized control and ensuring performance and safety.

Develop and maintain infrastructure for software development and AI operations, manage CI/CD pipelines, and automate configurations using various tools.

Lead technical pre-sales for enterprise contact-centre AI deals: perform discovery and architecture assessments, design solutions, run demos and POCs, author RFP/RFI technical responses, engage technical stakeholders, and ensure delivery readiness and competitive positioning across assigned region.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Owner of enterprise customer technical and commercial health: debug integrations, trace API/webhook failures, build dashboards with SQL, lead QBRs and renewals, drive expansion and product adoption, create runbooks, and relay technical feedback to Product and Engineering.

Own technical pre-sales for enterprise delivery deals: run discovery, build tailored demos and POCs, guide engineering through API integrations to first live orders, produce integration specs, respond to RFPs/security questionnaires, and hand off to post-sales while informing product roadmap.

Design, build, and maintain scalable full‑stack applications using React, TypeScript, and NestJS. Translate business requirements into architecture and design, contribute to system and API design, support CI/CD and DevOps practices, integrate AI coding tools (Claude Code) and agentic workflows, establish prompt engineering standards, and define guardrails for AI‑generated code. Work on large‑scale, production‑grade distributed systems.

Build and maintain Python tools for web scraping, automation, integrations (Airbnb, marketplaces), ad automation, chatbots (Chatwoot), workflow automation (Spiffworkflow), automated tests, and data analysis.

As a Staff Software Engineer, you'll architect and build foundational platform services, focusing on data infrastructure, healthcare interoperability, and secure APIs while mentoring other engineers.