Top AI Jobs in Toronto

Software engineering interns will complete scoped technical projects, build and test production-oriented code, contribute to Pinterest products and infrastructure, and collaborate with engineering, product, design, data, and mentors. Interns will develop professional skills, use AI responsibly in software development, participate in learning opportunities, and contribute ideas while gaining experience with large-scale systems.

Machine Learning Interns will own scoped ML projects from problem definition through experimentation, implementation, testing, and communication. They will develop and evaluate models for discovery, advertising, and search using techniques such as NLP and graph-based modeling; analyze data; contribute to applied AI research; write maintainable code; and collaborate with engineering, product, and data teams.

Evaluate AI model outputs, conduct audit-based reviews, identify patterns and failure modes, apply and improve evaluation guidelines, and document actionable findings. The role requires native-level Marathi, strong English, structured writing, GenAI safety knowledge, critical thinking, and comfort making judgment calls in ambiguous situations. Associates collaborate with leads and stakeholders, participate in calibrations, and help refine quality standards. This is a full-time, one-year, 100% onsite contract role in specified U.S. cities.

Lead Okta’s global partner product marketing and alliances strategy across ISVs, cloud providers, systems integrators, and resellers. Build joint value propositions, go-to-market content, sales enablement, demand-generation programs, and thought leadership focused on secure AI and identity solutions. Manage a global team and budget, measure program performance, collaborate with sales, product, alliances, and regional marketing teams, and maintain strategic partner relationships. Travel may reach 30%.

Leads Aviva Canada's national personal auto underwriting portfolio, setting underwriting, product, risk, regulatory, and modernization strategies. Oversees analytics- and AI-supported underwriting, regulatory filings, compliance, portfolio monitoring, product reviews, and profitable growth. Partners with Pricing, IT, Distribution, Operations, Digital, Claims, Risk, Legal, and Government Relations teams to improve automation, efficiency, customer experience, and broker service. Requires senior insurance underwriting leadership, regulatory expertise across Canada, strong analytical judgment, and stakeholder influence.

Lead architecture and delivery of scalable AI systems for P&C insurance, oversee fraud prevention platform from data to production, mentor AI engineers, collaborate with business stakeholders, prototype novel AI techniques (LLMs, GNNs, CV), and drive cloud deployments and responsible AI practices.

Designs and operationalizes enterprise AI, machine learning, and data solutions using Snowflake, Databricks, and Azure. Responsibilities include developing ML models, scalable data pipelines, MLOps and DevOps workflows, AI-ready data products, agent integrations, model deployment, monitoring, and data quality processes. The role also involves collaborating with business and technical stakeholders to deliver production solutions for client problems. This is a hybrid position requiring three days per week in a Mississauga or Montreal office or client location.

Productionize, optimize, and scale machine learning and LLM inference workloads across multi-cloud and Kubernetes environments. Optimize models using quantization, pruning, distillation, and hardware tuning; deploy GPU workloads; build routing, failover, APIs, monitoring, governance, and drift detection. Collaborate with platform, cloud, data science, product, and AI engineering teams to improve performance, reliability, capacity, and enterprise AI infrastructure.

Design, develop, deploy, and optimize enterprise AI and machine learning solutions, including generative AI, NLP, predictive analytics, RAG systems, AI agents, and LLM applications. Build scalable APIs and services, operationalize models through MLOps and DevOps, establish monitoring and governance, and collaborate with business, data science, engineering, infrastructure, and security teams. Translate business needs into technical solutions, support Agile project delivery, and maintain production AI systems.

Develop and enhance enterprise AI agents, orchestration workflows, conversational experiences, and tool-calling capabilities using Python, LangGraph, and LangChain. Integrate agents with backend systems through APIs, support MCP and A2A communication, and contribute to AI observability using Arize. Collaborate with engineers and subject-matter experts to improve platform reliability and deliver AI use cases.

Lead and grow a large AI engineering team building next-generation legal research capabilities. Set technical direction, productionize generative AI and machine learning systems, modernize scalable backend and data-processing platforms, and establish standards for reliability, observability, testing, experimentation, and AI evaluation. Partner across Product, Editorial, Research, and Engineering to deliver trusted AI-powered legal information workflows from prototype through continuous improvement.

Lead the integrated robotaxi program plan from autonomy and vehicle readiness through safety validation, fleet operations, regulatory approvals, and commercial launch. Manage cross-functional dependencies, risks, milestones, KPIs, executive reporting, and scalable tracking processes while coordinating engineering, safety, commercial, operations, and external partners.

Build AI-augmented, full-stack applications from concepts and prototypes through production, with emphasis on frontend excellence. Create accessible, responsive React, Vue, and Nuxt interfaces; develop APIs and backend services; manage SQL and NoSQL databases; implement authentication, caching, performance, and scalability strategies. Use AI design tools, Figma, design systems, and specification-driven workflows while collaborating with designers and innovation teams.

Leads customer-facing architecture engagements to modernize enterprise data platforms and build secure, governed, AI-ready solutions on Azure. Designs roadmaps across analytics, databases, data engineering, governance, and AI; guides proof-of-concept through production deployment; and supports migration, resiliency, performance, security, cost optimization, and scaled adoption. Partners with account, engineering, delivery, and customer success teams to remove technical blockers and develop reusable solution patterns.

Own complex post-sale technical relationships from onboarding through adoption and renewal. Lead implementations, integrations, configurations, and security reviews; advise strategic customers on identity, access, device trust, and SaaS security. Partner with Customer Success and cross-functional teams to drive customer value, manage risks, support expansion, create scalable resources, and mentor team members while serving as an escalation point.

Own the Admin Workflows area of 1Password’s Enterprise Password Manager, defining a roadmap for administrator capabilities, dashboards, reporting, notifications, recovery, user confirmation, and MCP features. Partner cross-functionally with Engineering, Design, Marketing, and go-to-market teams to ship API-centric enterprise features, address customer problems, drive business outcomes, and measure product success. The role requires strong cybersecurity expertise, technical API knowledge, leadership, communication, and enterprise product management experience.

Qualify farmers and prospects for FBN’s marketplace, crop protection, crop nutrition, seed, and livestock offerings. Conduct outbound calls, identify customer needs, communicate product value, manage sales pipelines, research regional markets, overcome objections, and document interactions in Salesforce. The role requires independent prioritization, strong communication, agricultural interest, and consistent sales performance in a remote environment.

Develop and optimize emulation and FPGA models for presilicon verification, postsilicon validation, and software development. Build models using synthesis, partitioning, and routing tools; debug hardware-software integration issues; create automation scripts and emulation methodologies; improve RTL development acceleration; support SoC and IP bring-up; and collaborate with design, silicon validation, and software teams.

Design and deploy AI-powered applications, agentic workflows, and enterprise automation solutions. Build data pipelines, APIs, microservices, and AI components using Python, SQL, and related technologies. Integrate LLMs, machine learning models, RAG, embeddings, vector databases, and agent frameworks in a regulated financial-services environment. Collaborate with architects, product owners, data scientists, and engineers while documenting solutions and researching emerging AI capabilities.

Leads TD Securities’ applied AI and innovation strategy, technical direction, product portfolio, and multidisciplinary AI team. Evaluates emerging AI capabilities and delivers production-ready solutions involving LLMs, agentic systems, retrieval, reasoning, and decision intelligence. Establishes rigorous experimentation, evaluation, governance, security, and responsible-AI standards while partnering with business, engineering, quantitative, risk, compliance, and technology teams. Drives adoption, measurable value, reusable AI components, and workflow transformation across capital markets.

Designs and leads production-grade generative AI, agentic AI, and machine learning solutions across cloud and edge environments. Establishes architecture standards, MLOps practices, observability, guardrails, and reusable frameworks. Advises product and engineering teams, develops proofs of concept, evaluates AI platforms, and integrates intelligent capabilities into grid automation and industrial systems. Requires strong experience with cloud infrastructure, AI/ML frameworks, deployment technologies, energy-system modeling, and full lifecycle model delivery.

Analyzes the exposure management competitive landscape, synthesizes technical and market intelligence, conducts win/loss analysis, supports competitive deals, develops field enablement materials, and recommends product and go-to-market strategies. Partners with Product, Sales, Marketing, Strategy, Investor Relations, and senior leadership to translate complex cybersecurity concepts into actionable guidance and executive insights.

Conduct applied AI research across foundation models, multi-agent AI, responsible AI, deep learning, NLP, predictive modeling, and generative AI. Evaluate research, develop and optimize large-scale models, and support production implementation using GPU-accelerated training. Collaborate with AI scientists, engineers, product leaders, academic researchers, and faculty to advance practical AI solutions. The role requires strong research, coding, publication, and interdisciplinary expertise.

Build and improve production AI workflows for residential mortgage underwriting. Responsibilities include agentic workflows, document intelligence, RAG, prompt and tool design, deterministic rules, model evaluation, testing, observability, security, human-in-the-loop controls, integrations, UAT, releases, incident analysis, and continuous improvement using Python, SQL, Azure, Databricks, and related AI services.

Leads and develops engineering teams while remaining hands-on with applied AI architecture, agents, prompts, evaluations, and production systems. Owns technical decisions, staffing, hiring, client engagement delivery, and converting lessons across projects into repeatable practices and platform capabilities. Requires strong software engineering depth, Python expertise, client-facing delivery experience, and familiarity with agentic and multi-agent systems.