Principal AI/ML Engineer
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Looking for a machine learning engineer job in Toronto? Choose from 23 open roles at 12 employers. Explore real salary data and skills breakdown across remote, hybrid and on-site roles.
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In the week to 27 September, 22 machine learning engineer roles were open in Toronto at 11 employers. 3 new roles were posted during the week. Of the roles that say how they work, 27% are remote or hybrid.
Among the machine learning engineer roles listed on the board, seniority skews firmly toward experienced practitioners: lead and senior levels each account for roughly equal shares and together make up most of what is listed, while intern and manager roles each represent a small minority. Hiring appears concentrated, with most roles sitting at a handful of employers rather than spread broadly across the market. As for freshness, a substantial portion of postings are a couple of months old, while about a fifth arrived in the past week and another fifth within the past month — so the board is a mix of recently opened and longer-standing vacancies. There is not yet enough weekly history to describe a direction over time. Large language model work features prominently across descriptions, suggesting employers are focused on modern generative AI applications rather than classical modelling alone.
Figures are measured every Monday.
Machine learning engineers build, train and deploy models that power data-driven products and services. Day-to-day work typically spans writing production-grade code, preparing and managing data pipelines, experimenting with model architectures and putting trained models into serving infrastructure. In a city where major financial institutions have their head offices and where organisations such as the Vector Institute conduct AI research, the role can appear in contexts ranging from enterprise risk and fraud systems to consumer-facing AI products. The presence of foundation-model companies founded locally, such as Cohere, and an active start-up ecosystem supported by bodies like MaRS Discovery District and the DMZ, means the work can span both research-adjacent and applied product engineering depending on the employer.
Large language models are mentioned in most descriptions listed here, making familiarity with LLM workflows a clear common thread. Python, fine-tuning and PyTorch each appear in roughly half of descriptions, alongside Spark and retrieval-augmented generation. Agentic system design, evaluation methodology and A/B testing appear in a meaningful share of postings, pointing to an expectation that candidates can move between model development and rigorous measurement of deployed systems.
On-site working dominates strongly among the roles listed here that state a location arrangement, accounting for most of what is posted. A small minority offer hybrid working. No roles currently listed state a fully remote arrangement, so candidates should expect to be based in Toronto for the large majority of positions.
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