Machine Learning Engineer Jobs in Toronto — Live Roles

Toronto's machine learning hiring sits inside a smaller set of employers than general software engineering — mostly teams embedding ML into an existing product (recommendation, fraud detection, personalization) rather than standalone AI-research labs, which concentrate more in Montreal. That changes what a posting is actually asking for: production ML experience (serving, monitoring, retraining a model that's already live) usually matters more here than research publications.

A resume aimed at this market does better leading with the data pipeline and deployment story, not just the model architecture. "Trained a model" is a weak bullet on its own; "owned the retraining pipeline for a fraud-detection model serving production traffic, cutting false-positive rate by a measured amount" tells a Toronto hiring manager you've carried ML past the notebook stage.

This filter can run thin on any given day — Toronto's ML-specific postings are a smaller slice of the city's total tech volume than general software engineering roles, and the live count shown above reflects exactly what's open right now, not a historical average. If the count looks low when you check, that's accurate, not a data gap; the broader Toronto software engineering hub linked below will have more volume if you're flexible on the ML-specific title.

Whatever the current count, the same rule applies before applying to any individual posting: run your resume's match score against it directly. ML postings vary enormously in what they actually weight — some want deep modeling experience, others want MLOps and infrastructure skills with only light modeling — and the gap analysis will tell you which one this is before you spend time tailoring a full application.