Top AI Jobs in Toronto

Lead design and rollout of evaluation, safety, and agent execution standards for AI features. Build reference implementations, monitoring, and cost controls. Mentor squads, partner with Product, Platform, Security, Privacy and Clinical teams, and co-develop governance to ensure production-grade, PHI-safe AI across the product.

Hands-on staff engineer who designs, builds, and ships AI-powered applications and orchestration. Leads architecture, platform development, integrations, and production delivery while ensuring security, observability, and measurable business impact.

Conduct independent research in speech and acoustic AI: design algorithms, perform literature review and data analysis, publish in top conferences, collaborate with cross-functional teams, present findings, and identify opportunities to apply research to Huawei products.

Design, develop, and implement AI-enabled solutions to automate and accelerate the software delivery lifecycle. Build AI agents and workflows using tools like Claude Code and Copilot, improve developer productivity, support production applications, troubleshoot incidents, and evolve SDLC into AI-driven ADLC while collaborating with stakeholders across delivery teams.

The AI Solutions Manager leads client engagements focused on AI and automation, managing project lifecycles, optimizing internal processes, and integrating AI tools. They also provide training and performance monitoring.

The Solution Architect designs AI solutions that automate workflows, collaborates with stakeholders, and integrates systems, ensuring continuous improvement and deployment.

Develop and train large-scale multimodal foundation models (vision and language) that fuse imaging with molecular data, build scalable distributed ML pipelines for petabyte-scale datasets, and collaborate with experimental scientists and engineers to productionize models and systems.

Lead development of AI-native production systems: convert AI-assisted prototypes into scalable, secure software; own architecture, reliability, and technical direction; direct and control AI coding agents; define engineering standards and workflows; collaborate with founders on product and strategy; debug complex issues and elevate engineering quality.

Create high-volume, high-quality marketing content using generative AI and AI agents (video, image, social). Develop timely campaign ideas, track AI and cultural trends, experiment with formats, and work closely with founders and the marketing team to shape brand voice and growth.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.

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.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

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.

Lead global data and AI engineering to deliver scalable, secure, and compliant data platforms and MLOps capabilities. Own strategy, architecture, operations, SRE practices, observability, and reusable self-service components to support analytics, AI, and enterprise programs.

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.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

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.

Design end-to-end cloud-native AI and data platform architectures enabling Digital Twin and Knowledge Graph solutions. Lead architecture discussions, define scalable platform patterns, collaborate with AI/data/infrastructure teams, evaluate emerging AI technologies, produce technical documentation, and support solution discovery and roadmap planning.

The Principal AI Consultant advises on AI application in Financial Services, focusing on operational relevance and business alignment, leveraging deep industry knowledge to enhance customer experience and efficiency.

Lead product strategy and roadmap for AI-powered seller tools (Magical Listing). Drive generative AI, image inference, automation, and experimentation to simplify listing creation, improve seller efficiency, and scale across web and mobile while building and mentoring a high-performing product team.

Lead end-to-end ML and data strategy across complex enterprise systems. Design and implement advanced statistical and machine learning solutions (including NLP and computer vision), architect data governance and lakehouse/warehouse integrations, drive AI evaluation and governance, and rapidly prototype solutions. Influence multiple teams, translate business needs into technical solutions, and discover ambiguous problems through user immersion to deliver measurable business impact.

The AI Resilience Fund Manager will establish a fund focused on resilient investments against AI disruption, lead capital raising, and advise investors on long-term strategies.

The role involves designing and validating data and AI architectures, collaborating with customers, and ensuring scalable, secure solutions are implemented in enterprise environments.

Develop and optimize software for IP customers, including firmware, drivers, and APIs. Collaborate with cross-functional teams for software integration and troubleshooting.