Top Tech Jobs & Startup Jobs in Toronto

Builds and ships production code, customer integrations, data pipelines, and analytics tools for enterprise accounts. Serves as the technical lead for several clients, gathering requirements, prototyping solutions, owning system performance and service levels, and communicating with technical and executive stakeholders. The role requires hands-on software engineering, cloud and data-platform experience, schema design, production monitoring, and approximately 30% travel to customer sites across the United States.

Own the production deployment and optimization of machine learning models at high request volumes. Responsibilities include reducing inference latency, improving throughput, profiling GPU code, containerizing models, designing distributed multi-GPU serving systems, and applying quantization, batching, caching, compression, and knowledge-transfer techniques. The role requires strong systems programming, GPU optimization, distributed systems, container orchestration, and production software experience.

Manages a small engineering team delivering a customer-facing product while contributing hands-on code. Responsibilities include technical design, roadmap planning, hiring, coaching, incident response, release reliability, and initiatives involving performance, analytics, experimentation, real-time data syncing, and generative AI workflows.

Lead the strategy and hands-on development of platform-wide design systems, including scalable interaction patterns, UX frameworks, mobile experiences, and AI-driven design-quality tools. Partner with engineers, product managers, and designers to improve design consistency and implementation quality across complex B2B products. Own initiatives from problem identification through prototyping and delivery while maintaining strong visual, interaction, accessibility, and code-adjacent design craft.

Manage Canadian immigration matters across economic, provincial nominee, family, work permit, and inadmissibility files. Counsel clients throughout applications, represent them when needed, and use AI tools to improve legal workflows and client service. The role offers potential progression into leadership or managing attorney responsibilities and requires Canadian legal licensure, immigration law experience, Canadian work authorization, and availability during Eastern Time hours.

Build and ship full-stack conversational AI features, integrating speech recognition, text-to-speech, and large language models into reliable production systems. Improve latency, reliability, cost, developer experience, and platform foundations at enterprise scale. Collaborate with product and machine learning teams, own initiatives end to end, incorporate production feedback, and support customer-facing engineering work.

Own and improve CI/CD, developer tooling, agent harnesses, Kubernetes infrastructure, observability, cost management, and incident-response practices. Build platform capabilities that reduce engineering toil and improve delivery speed for conversational AI products. Participate in an on-call rotation for core infrastructure and help shape reliable, scalable systems across a fully remote engineering organization.

Own and evolve the company’s visual identity across web, campaigns, social, sales collateral, events, and merchandise. Translate complex voice AI concepts into clear diagrams, illustrations, and marketing graphics. Build scalable brand systems, templates, and standards while partnering with product, marketing, and executive teams. Execute hands-on across digital, print, and experiential media with limited direction and high craft.

Own the company’s AI-powered quality and release infrastructure as its first dedicated hire. Build agent-run test harnesses, LLM graders, regression coverage, release gates, fresh-install and upgrade verification, and natural-language UAT. Manage CI health, artifact publication, deployment pipelines, credentials, certificates, and staging environments. Write production Python code, convert escaped bugs into regression tests, and provide trusted release-readiness evidence through dashboards, runbooks, retrospectives, and operating policies.

Conduct original research on reliable agentic AI, developing methods for simulation, evaluation, optimization, regression control, and continuous learning. Analyze agent failures, traces, and human feedback; create experiments and prototypes; and translate research into production-facing systems. Collaborate with product and engineering teams, contribute to technical strategy, and provide rigorous experimental evidence. The role requires strong research depth, publication or open-source contributions, hands-on Python development, and expertise in LLM agents or agentic systems.