Top Tech Jobs & Startup Jobs in Toronto

Drive revenue by identifying, qualifying, and nurturing enterprise AI leads. Conduct high-volume outbound outreach, qualify prospects using BANT, maintain Salesforce records, collaborate with marketing to refine messaging, and run end-to-end sales cycles on smaller opportunities.

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.

Build and scale ML-optimized HPC infrastructure, manage Kubernetes-based GPU/TPU superclusters, optimize for AI/ML training, and mentor teams while innovating in ML infrastructure.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

Lead the deployment of AI solutions for public sector clients, ensuring compliance with security standards while integrating AI into enterprise workflows.

Lead Cohere’s corporate development strategy across M&A, strategic partnerships, market expansion, and government relationships. Evaluate acquisitions and investments, lead due diligence and post-merger integration, develop market-entry and partnership frameworks, negotiate complex alliances, and create joint go-to-market strategies. The role partners closely with product, engineering, legal, finance, sales, marketing, policy, and government relations teams to support sovereign AI deployments, regulatory compliance, revenue growth, and international expansion.

Lead and mentor a GPU infrastructure engineering team to design, deploy, and scale GPU/TPU clusters. Define technical roadmap, implement topology-aware scheduling, monitoring, IaC, and cost/capacity optimization. Collaborate with AI researchers, cloud providers, and cross-functional teams to ensure reliable, secure, and performant infrastructure.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.