Top AI Jobs in Toronto

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Build and operate backend systems, APIs, integrations, automation workflows, and enterprise knowledge infrastructure for AI applications. Develop retrieval pipelines, vector database solutions, and agent-based systems while improving scalability, observability, reliability, and performance. The role requires production software engineering, modern AI architecture knowledge, clear documentation, testing, and collaboration with technical and nontechnical teams.

Own the architecture and technical strategy for Square’s go-to-market technology stack. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish AI reliability and security guardrails, define evaluation metrics, and guide build-versus-buy decisions. Partner with Sales, Product, Data, and Engineering leaders while mentoring engineers and remaining hands-on across design, implementation, testing, deployment, and operations.

Own the technical direction and implementation of authentication, authorization, and on-behalf-of delegation for Redpanda’s agentic AI platform. Extend its OAuth authorization server and token vault, build auditable delegation systems, and federate with enterprise identity providers such as Okta, Entra, and Ping. Lead technical decisions across multiple engineering teams, track emerging agent identity standards, collaborate with security and product teams, mentor engineers, represent technical positions externally, and deliver production-hardened systems.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

Manage retention, expansion, and cross-sell strategies for a portfolio of large law firms. Build relationships with partners, innovation teams, and C-suite leaders; lead consultative engagements; develop customized multi-product offerings; negotiate solutions; drive AI and technology adoption; and present ROI and commercial value while meeting revenue and pipeline targets.

Build automated growth marketing systems for enterprise and mid-market segments using AI, data enrichment, workflow automation, ABM, and intent signals. Responsibilities include lead scoring, CRM data management, outbound orchestration, seller operations automation, account tiering, regional workflow localization, vendor evaluation, and performance reporting against pipeline and SQL goals.

Own quality evaluation for LLM-driven and agentic products by building regression suites, golden datasets, scoring rubrics, and test harnesses. Detect hallucinations, tool-calling failures, prompt regressions, and unsafe behavior. Define reliability metrics, integrate evaluations into CI/CD, improve observability and tracing, contribute to API and end-to-end automation, and coach QA engineers on AI testing practices.

Own bug triage, code fixes, testing, and product quality monitoring across AI-powered products. Interpret user feedback, identify meaningful issues, validate fixes and edge cases, communicate status clearly, and document recurring problems. The role requires independent judgment, product sense, technical troubleshooting, and effective use of Claude Code and other AI tools. It is fully remote for US or Canada-based independent contractors, with potential conversion to full-time employment.

Leads accounting systems transformation and finance automation, including NetSuite and Salesforce optimization, AI agent deployment, integrations, reporting, and internal tooling. Owns project plans, testing, training, go-live, controls, and executive reporting while partnering with engineering and cross-functional teams. Identifies automation opportunities, pilots AI vendors, supports SOX requirements, and trains finance staff on new workflows.

Develops and manages customer-facing and internal technology platforms, including platform roadmaps, health, adoption, scalability, security, resilience, and support. Collaborates with technology, design, security, marketing, stakeholders, and vendors. Documents technical requirements, analyzes platform data, prioritizes enhancements, manages APIs, ensures compliance, and provides analytical and technical support. Requires cloud, DevOps, API management, system design, product management, cybersecurity, and data analysis proficiency.

Build and own Android growth and monetization experiments across subscriptions, advertising, purchases, and upgrade flows. Partner with product, design, data science, and analytics to instrument experiments, evaluate results, and ship reliable experiences at consumer scale. Use AI-native workflows for specification, coding, testing, and review; improve mobile engineering practices; contribute to architecture and code reviews; participate in on-call; and mentor engineers.

Develop and roll out core hardware platforms, define hardware architecture, evaluate next-generation chipsets, and manage approved vendor lists. Perform full-lifecycle hardware design and validation, including signal and power integrity reviews. Collaborate with mechanical, software, RF engineering, and procurement teams to resolve system constraints, improve supply chain robustness, and ensure design for manufacturability.

Lead research for AI-native data platforms, including vector data lakes, intelligent caching, RAG retrieval, agent memory, knowledge bases, indexing, hybrid search, and context freshness. Design prototypes, publish at leading venues, secure patents, collaborate with product and engineering teams, represent the organization externally, and mentor researchers and PhD interns.

Own full-cycle sales for Featherless’s AI inference cloud, from prospecting and discovery through technical demos, proposals, negotiation, and close. Build relationships with CTOs, ML engineers, and infrastructure leaders; close mid-market and enterprise deals; expand existing accounts; maintain HubSpot CRM accuracy and forecasting; collaborate with CRO, BDR, DevRel, product, and engineering; and help establish repeatable sales and go-to-market processes.

Build and qualify enterprise and AI startup pipeline, manage inbound requests and outreach automation, re-engage users, track CRM and pipeline metrics, and attend events to generate relationships. The role requires technical fluency in AI, strong communication, entrepreneurial initiative, and comfort engaging developers, founders, researchers, and enterprises. Candidates should have 1–2 years of experience in sales, developer relations, or entrepreneurship.

Build and deploy AI-native tools across internal functions including Operations, Finance, HR, and Legal. The role embeds with business teams to identify high-impact opportunities, prototype and scale LLM, retrieval, and agentic solutions, evaluate build-versus-buy decisions, define ROI, and drive adoption. Responsibilities include production engineering practices, data modeling, automation, AWS application development, stakeholder translation, team training, and measuring post-launch impact.

Build and operate production AI systems for go-to-market teams, including account segmentation, scoring, enrichment, buying-signal detection, agentic workflows, prospect research, outbound personalization, call summarization, and CRM hygiene. Own solutions end to end from design through deployment, while optimizing quality, latency, cost, reliability, evaluation, monitoring, and guardrails. Collaborate closely with Marketing, Sales, RevOps, and system users to deliver measurable commercial impact.

Leads enterprise AI and data architecture strategy, defining target architectures, standards, governance, roadmaps, and infrastructure requirements. Designs end-to-end AI solutions spanning data pipelines, model training, deployment, and monitoring. Advises business and technology stakeholders, evaluates emerging technologies, ensures regulatory and architectural compliance, manages risks, presents recommendations to leadership, and mentors technical teams. Requires extensive experience in AI/ML architecture, data engineering, cloud platforms, MLOps, and enterprise technology strategy.

Lead company-wide AI capability and adoption by developing strategy, coordinating a cross-functional AI Superteam, designing role-based learning, driving change management, scaling successful use cases, and measuring AI readiness, usage, and impact. The role partners with senior stakeholders across business, technology, security, and legal teams to promote responsible, practical AI use and embed new ways of working across the organization.

Own outbound top-of-funnel activities for skilled nursing or home health and hospice accounts. Generate qualified meetings through cold calling, LinkedIn, email, creative campaigns, and industry events. Build contact lists, messaging, and multi-touch sequences from scratch; use AI and buying signals to prioritize accounts; and collaborate with Account Executives, RevOps, and Marketing to develop territories and sales plays.

Build and deploy enterprise automation and AI solutions across business systems, data platforms, and IT environments. Analyze business processes, identify automation opportunities, integrate platforms and APIs, test and monitor production solutions, and support adoption through documentation and training. The role requires hands-on coding, AI workflow experience, process analysis, and the ability to translate business needs into secure, practical technical solutions.

Own and expand Jobber’s AI marketing enablement program by building agents, automations, integrations, evaluation practices, and reusable workflows. Partner with marketing teams to identify high-impact use cases, connect AI to live data, improve campaign analysis and execution, coach marketers, and drive adoption. The role also owns the AI roadmap, maturity model, documentation, reliability, responsible use, and measurement of business impact across Demand Generation and broader marketing.

Owns production AI systems end to end, including evaluation pipelines, agents, retrieval, routing, model selection, data feedback loops, and observability. Diagnoses failures across audio, prompts, models, and infrastructure; improves quality through experiments and production debugging; and leads safe rollouts. The role establishes applied AI engineering standards, translates clinical problems into measurable plans, and delivers reliable systems used by healthcare clinicians.

As an Applied Scientist, you will push the limits of AI/ML in home valuation, tackling diverse ML challenges and optimizing decision-making with data.