Top AI Jobs in Toronto

Design and architect multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows and autonomous operations.

Run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tool use, and support RLHF alignment and constitutional AI development to ensure safe AI deployment.

Lead product strategy and roadmap for AI and robotics products, translating VLA and agentic RAG breakthroughs into customer value, defining safety/usability/performance metrics, working with enterprise adopters, and driving cross-functional delivery.

Optimize, compress, and distill large language and vision models for on-device inference. Build pipelines for distillation and hardware-specific compilation, and benchmark performance across NPU/GPU architectures.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Independent full-cycle recruiter managing sourcing, client engagement, offer negotiation, and placements using AI-driven tools. Acts as commercial advisor, provides market insights, and maintains self-directed billing and activity in a results-only, remote environment.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Build production-grade AI agent systems, automations, and workflows across recruiting, security/compliance, finance, onboarding, CRM integrations, and growth. Implement LLM-powered solutions, API integrations, and operational tooling to improve hiring, sales, and enterprise readiness.

The role involves applying diffusion techniques and developing AI models to enhance 3D reconstruction pipelines for various industries.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Own and run an end-to-end AI UGC pipeline to produce dozens of TikTok/IG ad variants weekly: ideation, avatar/voice generation, editing, captions, publishing, and rapid A/B testing. Measure hook and hold rates, CPM/CPA, iterate quickly, maintain account health and policy/compliance (supplements, AI disclosures), and continually adopt new tooling to scale top-performing creatives.

As an AI Platform Backend Engineer, you'll design and deploy backend systems utilizing AI for real-world applications, optimize performance, and mentor junior engineers while working directly with clients to address complex challenges.

As an AI Platform Engineer, you will design and develop AI-driven backend solutions, optimize performance, and collaborate with customers and teams.

Design and ship a 14-domain skills taxonomy and SDK/CLI tooling, operate the MCP server, integrate agent protocols, improve LLM grounding for crypto, run developer hackathons, and produce technical content to grow the ecosystem.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Research AI threats, analyze threat actor behavior, produce actionable intelligence, collaborate with teams, and publish findings in the cybersecurity domain.

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.