Toronto AI Jobs in 2026: 81 Open Roles and a Hiring Story San Francisco Can't Match
The AI hiring map everyone carries in their head centers on San Francisco, with New York as the secondary market and London as the European outlier. That mental model leaves out one of the most consequential markets on the board.
Toronto is currently the #4 AI/ML hiring market in the world. HireIndex’s index of 1,789 open AI/ML roles across 748 companies (as of May 18, 2026) places Toronto at 81 open roles — ahead of Seattle (40), Paris (28), and every other city outside the top three.
The more striking comparison: San Francisco has 129 open AI/ML roles in the same index. But OpenAI accounts for 65 of them — just over half. Strip OpenAI out and San Francisco’s market depth is 64 roles. Toronto’s 81 is larger than the entirety of Silicon Valley’s AI hiring outside its single largest employer.
How many AI jobs are open in Toronto right now?
81 open AI/ML roles as of mid-May 2026, placing Toronto at #4 globally:
| City | Open AI/ML Roles |
|---|---|
| London | 173 |
| San Francisco | 129 |
| New York | 127 |
| Toronto | 81 |
| Sydney | 77 |
| Seattle | 40 |
| Paris | 28 |
This is not a rounding error or a data artifact. Toronto’s 81 reflects consistent demand across multiple company types — AI labs, enterprise software, financial services, and consulting firms. The market is as real as the ranking.
The comparison that sticks: Sydney (#5, 77 roles) is often cited as a surprise in these rankings. Toronto at #4 is a bigger surprise. Candidates optimizing their search for US markets are competing in a more crowded pool while leaving a substantial adjacent market untouched.
Which companies are hiring AI talent in Toronto?
Toronto’s AI hiring splits into three distinct employer types, each with different roles and different hiring logic.
AI labs with Canadian roots. Cohere — the enterprise LLM company founded in Toronto in 2019 — maintains significant Canadian headcount alongside global expansion. The company runs ML internship and co-op programs with genuine conversion pipelines, and posts roles in both Toronto and London. Cohere’s Toronto presence gives the city a frontier-adjacent hiring pipeline that most non-US markets lack. Their work on enterprise LLM deployment covers the production ML engineering, fine-tuning, and evaluation disciplines that the broader industry is now scaling.
US tech majors with established Canadian engineering hubs. Amazon operates major engineering centers in Toronto and Vancouver, with Applied Scientist and Machine Learning Engineer roles integrated into the same teams as its Seattle and New York hiring. Of the 20 Applied AI roles Amazon posts in the current HireIndex index, Toronto captures 8 — a significant share of Amazon’s Applied AI volume outside its primary US markets. These aren’t satellite positions or satellite offices. They’re fully embedded in Amazon’s product and science organizations, working on the same search ranking, pricing science, and conversational AI work as their US counterparts.
Financial services. Toronto is Canada’s financial capital, and its major banks — RBC, TD, BMO, Scotiabank — are all running AI hiring programs. These don’t yet surface in the same volume as Capital One’s 13 New York-based LLM Engineer openings, but they represent a structural hiring floor that won’t evaporate with the next funding cycle. Banks hire slowly and deliberately. When they commit headcount for AI engineering, those seats tend to stay open.
What does AI work in Toronto actually look like by title?
Toronto’s title distribution tracks closely with the overall HireIndex index, with two notable departures: Applied AI is overrepresented and LLM Engineer is nearly absent.
Toronto has 8 Applied AI roles — roughly 9.9% of its local market, versus the 7.5% Applied AI share across the full index. Most of these trace to Amazon’s embedded Applied Scientist pipeline, where Toronto is one of the preferred locations for the kind of work the applied-ai analysis describes as “classic applied research embedded inside existing product and operations teams” — experimentation, modeling, and measurement against an internal product roadmap.
LLM Engineer is the notable gap. The entire HireIndex index has only 37 explicitly-tagged LLM Engineer roles, and Toronto claims one of them. The explicit title is concentrated in New York (12 roles, almost all Capital One) and London (7 roles, mostly JPMorganChase). Toronto’s financial sector is doing LLM work — but advertising it under different labels. That’s a search problem, not a jobs problem. The same labeling lag applies in reverse: candidates filtering strictly by “LLM Engineer” miss the majority of LLM-adjacent work posted under Applied Scientist, ML Engineer, or AI Platform titles.
How does Toronto compare on seniority?
One junior role out of 81 total — a 1.2% junior rate, below the market-wide 2.6%. The seniority floor is mid-level, same as everywhere.
The exception worth noting: Cohere runs a real ML internship and co-op program. For early-career candidates willing to work outside the US, this is one of the few frontier-adjacent companies with genuine entry-level volume. Converting a Cohere co-op into a full-time offer is a real career path — the company’s production LLM work gives engineers experience that transfers directly to the rest of the industry. It is also one of the few ways into a frontier-adjacent AI role that doesn’t require either a US work permit or a PhD.
Why does Toronto’s AI market punch above its weight?
Three structural factors explain why 81 open roles exist in a city that doesn’t appear in most candidates’ top-five target lists.
The university pipeline. The University of Toronto and the Vector Institute are among the most productive ML research environments in the world. Geoffrey Hinton’s presence at U of T for decades created an institutional pipeline of ML-credentialed graduates entering Canadian industry. That pipeline continues to produce; the output enters both Canadian companies and US companies with Toronto engineering presence.
Immigration infrastructure. Canada’s Express Entry and Global Talent Stream programs process AI talent from outside North America faster — often significantly faster — than US H-1B timelines. For companies that need engineers from India, Eastern Europe, or Southeast Asia, Toronto is frequently the path of least resistance. Several US-headquartered companies maintain Toronto offices that function partly as talent access points: the role is in Toronto because that’s where a specific engineer can legally work.
Cost structure. Senior AI engineers in Toronto typically earn 20-30% less than US equivalents at comparable companies, on a cost-adjusted basis. For organizations that are technically serious but budget-constrained — mid-stage startups, Canadian financial institutions, consulting firms doing genuine AI work — this opens hiring optionality that’s closed in San Francisco or New York. The talent quality is comparable; the compensation anchor is not Silicon Valley.
What should AI job seekers know about Toronto?
Toronto is a real market, not a backup. If you’re an international candidate who needs Canadian work authorization, or a US candidate open to relocation, 81 open roles at companies like Cohere and Amazon represent genuine career-relevant opportunities — not a consolation prize.
Amazon’s Toronto presence is underappreciated. Eight Applied AI roles embedded in the same Applied Scientist pipeline as Seattle and New York is significant volume. If you’re targeting Amazon’s applied science track, filtering by Toronto alongside US cities meaningfully expands your option set without changing the nature of the work.
The LLM title gap is a labeling issue, not a demand issue. Toronto’s financial sector is building LLM-based products. The postings use Applied Scientist, ML Engineer, and AI Platform titles rather than “LLM Engineer.” Filtering by company type and reading job descriptions will surface this work; filtering by title alone will not.
Remote Remote still dominates, but Toronto is one of the strongest in-person markets outside the US. If in-person or hybrid work is important — for immigration reasons, for networking, or for team structure — Toronto offers a depth that Seattle, Paris, and Berlin don’t come close to matching right now.
Browse current openings filtered by role: Machine Learning Engineer for the highest-volume category, or Applied AI for the Amazon-and-Cohere dominated segment. Data updated every Monday from direct ATS and aggregator sources.
HireIndex tracks 1,789 open AI/ML roles across 748 companies as of May 18, 2026.