Top AI Jobs in Toronto

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.

Early-career SDET on Release Integration Testing for Cerebras AI Inference Core. Write automation and tests across the AI stack, triage cross-stack failures, maintain branch stability, collect qualification evidence, and collaborate with feature and infra teams to prepare features for production release.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Lead and mentor an AI engineering team to design, build, and deploy production-grade LLM, NLP, and generative AI solutions on Azure. Architect RAG frameworks, conversational agents, and scalable AI workflows; collaborate with stakeholders to translate business needs into reliable, monitored AI services.

Lead and mentor an AI engineering team to design, build, and deploy production-grade LLM, NLP, and generative AI solutions on Azure. Architect RAG frameworks, conversational agents, and scalable AI workflows; engage stakeholders, validate model performance, and support AI governance and continuous improvement.

Lead and mentor an AI engineering team to design, build, and deploy production-grade LLM, NLP, and generative AI solutions. Architect Azure-based AI systems (Azure OpenAI, Azure AI Services), implement RAG and vector DB workflows, develop conversational agents, collaborate with stakeholders, and establish testing, monitoring, and governance for continuous improvement and reliability.

Lead design and implementation of Northleaf's AI-ready knowledge architecture: taxonomy, metadata, content governance, onboarding, and quality testing to enable secure, repeatable AI/search workflows across SharePoint and Azure-based platforms.

Design, prototype and deliver agentic AI solutions and automations using Microsoft Copilot, Copilot Studio and Power Platform. Partner with business teams to map processes, build production-ready agents, enable integrations, and create reusable templates and orchestration patterns to drive adoption and enterprise-scale automation.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Design and implement advanced Generative AI models, optimize performance for LLMs, and collaborate with cross-functional teams to innovate AI solutions within the automotive sector.

As a Backend/AI Systems Engineer, you will develop backend systems for AI workflows and enhance core functionalities for Spellbook's legal software. You'll tackle complex problems, ensure system reliability, and collaborate with product teams, while maintaining a hands-on ownership approach.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Instructional Designer is responsible for creating engaging learning experiences, managing the instructional design lifecycle, and collaborating with SMEs and developers to deliver effective training programs.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Lead operational rhythms and client delivery, focusing on AI solutions. Collaborate with executives and manage multi-stakeholder projects for positive outcomes.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

Lead enterprise platform and AI sales across healthcare segments, own end-to-end sales cycle and quota, build pipeline and C-level relationships, negotiate complex agreements, collaborate with Solution Engineering and Product Marketing, surface product feedback, improve buyer journeys, and ensure smooth handoffs to delivery teams.

Lead AI validation strategy for LLMs, RAG, and agent systems. Build automated CI/CD validation pipelines, design semantic and prompt regression tests, implement guardrails and confidence scoring, monitor production AI quality, and partner with engineering and data governance to ensure safe, accurate, production-ready AI.