Top AI Jobs in Toronto

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 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 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.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead the design and delivery of an end-to-end AI system layer across the product: agentic workflows, orchestration, evaluation, guardrails, observability, and developer platform. Build and grow a multi-team org, partner with Product and Design, define how AI decisions flow across cross-product context, and ensure reliable, measurable, and safe autonomous behaviors that proactively help small-business users.

Perform hands-on product and player analytics across onboarding, retention, monetization, progression and LiveOps. Build and QA ETL pipelines in an AWS analytics stack, write cost- and performance-aware SQL, support A/B testing, and create reusable AI-enabled analytical workflows and notebooks to inform product, marketing, and LiveOps decisions.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Lead development of agentic AI features and shared platform components for a multi-tenant SaaS finance platform. Design LLM orchestration, CI/CD, deployment pipelines, observability, scalable background processing, ingestion and search systems, and PostgreSQL optimization. Drive technical vision, mentor engineers, and deliver production-grade, secure, and observable AI-first solutions end-to-end.

The role involves developing and productionizing security detection models, collaborating with security research, and shaping engineering culture as a founding team member.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Lead engineering to shift GTM from no-code automation to code-first, AI-agent-driven systems. Design scalable backend pipelines, integrate CRMs/APIs, build reusable agent frameworks, ensure reliability and observability, and hire/mentor a distributed engineering team to deliver production GTM infrastructure.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Lead high-volume outbound prospecting, run discovery calls, qualify and manage high-ticket opportunities through the full sales cycle, book and conduct meetings, handle objections, collaborate with founders/sales leadership, and consistently meet outreach, meeting, and revenue KPIs.

The Senior AI Solutions Engineer will architect and deploy AI systems on Google Cloud, collaborating with clients and delivery teams to create enterprise-ready solutions.

Build and deploy high-impact AI-native products, architect agent-driven workflows, and collaborate with teams to integrate cutting-edge AI solutions into production systems.

As AI Resilience Fund Manager, you'll establish and manage a fund focused on resilient investments, lead fundraising, and advise investors on navigating challenges created by AI and economic shifts.

Remote contract role applying equity research expertise to train and evaluate AI models. Review AI-generated financial analyses, analyze SEC filings and financial statements, create evaluation tasks and structured datasets, and collaborate with AI researchers to improve financial reasoning and assessment criteria. Minimum 2 years equity research experience; 10 hours/week minimum commitment.

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.