Top Tech Jobs & Startup Jobs in Toronto

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.

Call for fresh graduates and students to submit a one-page resume and a cover letter (max 500 words) to register interest in entry-level and internship roles in Web3 and AI. Applicants should state country/visa status, desired sectors and roles, and can include portfolios or personal projects to demonstrate relevant skills.

Lead ecosystem and partnership strategy to drive integrations, DeFi onboarding, RWA issuer engagement, and institutional adoption. Own partner lifecycle, negotiate strategic deals, coordinate cross-functional integrations, and translate partner feedback into product and go-to-market improvements while representing the platform externally.

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.

Design and implement multi-agent pathfinding algorithms to avoid fleet deadlocks, build low-latency Robot-to-Cloud and Robot-to-Robot communication protocols, and develop cloud dashboards for fleet monitoring and teleoperation handoffs.

Design and implement high-fidelity spatial mapping and real-time object recognition pipelines. Integrate vision systems with robotic manipulation and navigation controls. Train and fine-tune Vision-Language-Action models and work with multimodal spatial AI frameworks and robotic hardware.

Design and implement advanced control algorithms for bipedal locomotion and manipulation, train RL policies in simulation, deploy on hardware, and perform sim-to-real transfer and gait/kinematics analysis.

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.

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.

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.