Top Python Jobs in Toronto

Seeking a Sr DevOps Engineer to design and maintain secure, scalable AWS cloud infrastructure, manage CI/CD pipelines, and improve system reliability across advanced data analytics platforms.

Own technical execution of customer data migrations: explore source systems, export data, author declarative version-controlled mappings, transform and validate data, run end-to-end migrations on internal platform, build reusable templates/validation, troubleshoot discrepancies, and document mapping and transformation logic for auditable production cutovers.

Lead test strategy and build scalable automated test frameworks for a custom Linux kernel. Validate performance, reliability, and security; develop test suites for kernel enhancements and VM software; debug low-level system issues; enhance CI/build automation; provide technical leadership and mentor junior engineers.

Lead high-impact AI solutions, overseeing architecture and deployment. Partner with stakeholders, coach engineers, and drive project success from inception to launch.

Lead a team of cloud engineers to design, implement, secure, and optimize cloud-native landing zones on Azure and GCP. Own IaC (Terraform/Ansible), container orchestration (Kubernetes/Docker), CI/CD, monitoring, cost and security optimization, cross-functional integration, documentation, and mentoring.

12-16 week internship implementing and optimizing GPU/accelerator kernels and custom operators, profiling and improving LLM inference and runtime performance, building benchmarks, fixing regressions, and shipping code to production or open-source AI infra projects.

Research, design, and train foundation models for robotics; align text-based reasoning with low-level control policies; evaluate zero-shot generalization; run experiments; integrate models into robotic systems and present findings externally.

Develop and evaluate high-resolution tactile sensing and dexterous manipulation systems by processing tactile and force-torque data, implementing slip-detection and compliant control, training multi-fingered grasping policies, integrating solutions into robotic platforms, and conducting experiments to validate performance.

Optimize, compress, and distill large language and vision models for on-device inference. Build pipelines for distillation and hardware-specific compilation, and benchmark performance across NPU/GPU architectures.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Build and maintain end-to-end web applications powering sales and operations. Design tools for quoting, scheduling, and fulfillment, integrate AI into products, collaborate on UX, ship high-impact features, and contribute to architecture, deployment, and engineering culture.

The role involves building and scaling growth engineering products, balancing quality and speed effectively, and influencing product direction and AI-driven growth solutions.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

The Director of Epidemiology Pipeline collaborates on virology clinical trials, providing scientific expertise, leading project proposals, and overseeing study management and regulatory compliance.

Build production-grade AI workflows, automations, and agent systems across recruiting, security/compliance, operations, finance, and GTM. Design LLM-powered systems, API integrations with CRMs and workflow tools, and automation that reduces manual overhead and improves enterprise readiness and customer experience.

Build production-grade AI agent systems, automations, and workflows across recruiting, security/compliance, finance, onboarding, CRM integrations, and growth. Implement LLM-powered solutions, API integrations, and operational tooling to improve hiring, sales, and enterprise readiness.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

The ML Engineer will integrate model architectures, optimize deployment workflows, maintain CI/CD pipelines, and ensure reliability of inference services across large-scale systems.

As a Software Engineer in Reliability, you'll architect and manage multi-cloud GPU infrastructure, ensuring performance, security, and scale while debugging complex hardware/software issues.

The Research Engineer will design scalable evaluation systems for multimodal generative AI models, integrating metrics and automated pipelines for model assessment and improvement.

Lead and hands-on build Luma's inference serving platform: architect scheduling, routing, autoscaling, and observability across thousands of GPUs; operate and optimize heterogeneous fleets; ship new model architectures from research to production; own SLOs, cost, incidents, and team hiring/mentorship.

The role involves using AI tools to rapidly develop software, working across product and engineering teams, and owning software projects end-to-end.

Contribute to the reliability and performance of Mithril's GPU orchestration platform through automation, observability, and infrastructure management. Collaborate with the team to ensure scalability across multi-cloud environments while maintaining systems stability and implementing SLOs.

The Research Scientist/Engineer will innovate algorithms for real-time adaptation, optimize ML systems, and ensure impactful product interactions.