Top Tech Jobs & Startup Jobs in Toronto

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Build scalable, accessible frontend components and UX for an AI-native data platform using React and Next.js. Implement dashboards, agent interfaces, and multi-tenant admin panels; integrate real-time and streaming data, APIs, and visualize high-dimensional data. Contribute to component libraries, design systems, and ship production-ready, responsive features.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, build, and operate secure, tenant-isolated cloud infrastructure and CI/CD for an AI-native multi-tenant platform. Implement observability, policy-as-code, SLAs, incident response, and automate provisioning (Terraform/Helm). Support deployment, monitoring, and secure operations for ML/agent systems at scale.

Founding full-stack engineer to architect and build a production platform combining large-scale data systems and AI-native agents. Work across backend, frontend, data pipelines, agents, and deployment systems; define architecture, devops, CI/CD, and early engineering processes while shipping features and growing the team.

Lead end-to-end product design for a B2B SaaS platform connecting automotive companies and suppliers. Define product features, design AI-driven workflows and scalable systems, build design system components, prototype and hand off to engineers, and run lightweight user research to iterate on usability and impact.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Drive go-to-market for automotive customers by researching strategic accounts, building ROI models and commercial proposals, tracking procurement and supplier ecosystems, analyzing competitors, creating executive materials, and supporting end-to-end business development efforts with AI-accelerated workflows.