Top Python Jobs in Toronto

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

Design, develop, and optimize big data solutions using Databricks, Apache Spark, and cloud platforms. Collaborate with teams to ensure high-performance data architectures.

Lead design, development, and maintenance of large-scale Python-based generative AI platforms and services. Collaborate with data science, technical, and business stakeholders to define architecture, deploy GenAI products, enforce coding standards and testing, mentor mid-level engineers, manage multiple projects, and ensure governance, risk, and controls for enterprise-grade AI solutions.

Lead post-silicon power characterization and correlation for semiconductor products, develop measurement strategies, and improve power architectures to meet specifications.

Design, build, and optimize lakehouse data pipelines and curated data products using Python and SQL. Improve performance, observability, governance, and cost-efficiency. Own data modeling, orchestration, data quality, and secure access patterns. Partner with product, engineering, and analytics teams, troubleshoot production issues, and mentor junior engineers to advance the organization’s data platform and AI readiness.

Lead technical design, architecture, and delivery of backend and platform services for AI agent workflows. Own cloud infrastructure, IaC, CI/CD, Kubernetes/EKS, and managed AI runtimes. Improve reliability, observability, performance, and cost efficiency; mentor engineers; drive cross-functional initiatives and participate in on-call support.

Design, build, and operate high-performance distributed inference systems that route requests, autoscale compute across accelerators, deploy new models, integrate new hardware, and tune performance for millions of users while enabling research workflows.

Design, build, and scale security infrastructure across systems, networks, endpoints, and products. Own security architecture, detection/response, vulnerability and risk management, and security policies. Integrate security into the SDLC, partner with engineering, run incident response and monitoring, lead security education, and temporarily own IT infrastructure.

Support threat intelligence, incident analysis/response, penetration test coordination, vulnerability scanning and remediation tracking, security tool administration, dashboard maintenance, and adherence to security policies while learning and developing cybersecurity skills.

Senior Software Security Engineer responsible for threat modeling, security architecture reviews, code and penetration testing, integrating SAST/DAST/SCA into CI/CD, triaging vulnerabilities, driving secure SDLC/DevSecOps practices, incident response, and ensuring compliance with industry security standards.

Build and run web-scale data pipelines and crawlers to surface AI-generated content and hallucinations. Use detection models and LLMs to rank and classify findings, synthesize results into compelling prose, visuals, and web displays, and improve models based on investigation outcomes. Automate monitoring of social feeds and support investigative reporting workflows.

Own and automate the end-to-end ML lifecycle: build, deploy, monitor, test, and retire models at scale. Design production-grade, highly available ML infrastructure and CI/CD pipelines, implement governance, and collaborate with engineering and security teams.

Build, operate, and scale cloud-native backend services and APIs for a high-volume vehicle marketplace. Embed AI-driven capabilities into features, use AI tools to accelerate development, participate in on-call rotations, improve reliability, and collaborate with Product, Data, Infrastructure, and SRE teams to deliver production-ready systems.

Design and drive security architecture for applications, services, and infrastructure. Partner with engineering and DevOps on secure backend design, cloud and container security, CI/CD hardening, and access controls. Conduct threat modeling, risk assessments, and security reviews; integrate security tooling; enforce secure coding practices; mentor engineers; and stay current on fintech and blockchain threats and compliance standards.

Lead definition and execution of a company-wide experimentation and measurement platform. Build and scale centralized experimentation infrastructure (Eppo), integrate with the data ecosystem, establish governance, tooling, lifecycle management, measurement standards, and partner with Product, Data Science, Analytics, and Strategy to drive rigorous experiment-driven decision-making.

Work directly with enterprise customers to design, implement, and scale data intake and workflow solutions on the Feathery platform. Own technical delivery from discovery to production, build prototypes and demos, advise customers on best practices, create implementation documentation, and help scale repeatable playbooks while partnering cross-functionally with Sales, Product, and Engineering.

Design and implement product-level authentication, authorization, multi-tenant isolation, and privacy-preserving data lifecycles. Ship security-sensitive changes end-to-end, own application and API security, threat modeling, telemetry for security events, and integrate conservative partner security controls.

Work with founders to solve cross-functional problems (ops, GTM, partnerships). Build and optimize scalable processes, set and track operational KPIs, lead end-to-end projects, and analyze data to provide insights that improve product and customer experience.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Own QA for web and mobile releases: write and run manual and automated tests (web, mobile, API), build test frameworks, improve CI, track issues, mentor teammates, and influence requirements and quality processes.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Lead-platform principal engineer who codes, debugs, and sets reference implementations across AI-powered CX systems. Own platform architecture decisions, unblock squads by shipping production code, mentor engineers, and build high-capacity, event-driven distributed systems integrating messaging, multiple DB types, multi-cloud, and advanced AI (LLMs, RAG, agents).

Lead a squad as a player‑coach building event-driven, high-throughput, low-latency platform services and AI/ML pipelines. Implement Go services, connectors, and real-time WebSocket backends; own latency budgets, delivery, code quality, and weekly demos.

Lead day-to-day engineering for an AI-first SaaS platform, owning delivery, hiring, squad performance, architecture reviews, cross-team dependencies, and driving AI-assisted engineering practices to ship weekly demos at high quality.