ML Engineer Salary in Toronto 2026: CAD $110K-$280K Complete Guide

Toronto has quietly become one of the world's most important cities for machine learning research and applied AI - and compensation is finally beginning to reflect that. From Geoffrey Hinton's legacy at the University of Toronto to Cohere's rise as a serious LLM company competing globally, Toronto's ML ecosystem has graduated from academic hub to commercial powerhouse.

This guide covers exactly what ML engineers earn in Toronto in 2026, broken down by experience level and employer, the CAD vs USD gap every engineer must understand, and how Toronto stacks up against US hubs and other Canadian cities.

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Toronto ML Engineering Market Overview

Toronto's ML ecosystem is anchored by several distinct forces that collectively create a layered job market:

Cohere is Toronto's highest-profile pure-play AI company and arguably the most important ML employer in Canada. Founded by former Google Brain researchers, Cohere builds enterprise LLM infrastructure and competes directly with OpenAI and Anthropic. Compensation at Cohere is structured to attract talent that would otherwise go to US companies - base salaries for senior ML engineers typically run CAD $180K-$240K with meaningful equity upside. For Toronto-based ML engineers who want frontier LLM work without relocating to San Francisco, Cohere is the benchmark. Vector Institute is the research anchor of Toronto's AI ecosystem. Affiliated with the University of Toronto and co-founded by Geoffrey Hinton and Yoshua Bengio, Vector publishes foundational ML research and trains the next generation of Canadian AI talent. Compensation at Vector is below commercial market (CAD $95K-$160K for research scientists) but the research freedom and publication opportunities are unmatched in Canada. Shopify is the largest technology employer in Canada by headcount and applies ML across payments fraud detection, search ranking, merchant analytics, logistics optimization, and AI-powered commerce tools. Shopify's ML teams are distributed but Toronto-anchored, and compensation has become increasingly competitive: senior ML engineers earn CAD $175K-$250K total comp, with Shopify's equity program providing meaningful upside. RBC's Layer6 is Canada's most respected finance AI team. Acquired by Royal Bank of Canada in 2018, Layer6 applies deep learning to financial products - credit risk, personalization, fraud detection - with a research culture more similar to an AI lab than a bank. Layer6 pays CAD $150K-$210K for senior ML engineers, with the stability and benefits of a major financial institution. Google Brain Toronto / Google DeepMind maintains a significant research presence in Toronto. Google's Toronto office has historically focused on fundamental ML research and attracts researchers who want to work on long-horizon problems. Google's ML compensation in Toronto follows its global bands, making it one of the highest-paying employers in the city: senior research engineers earn CAD $200K-$280K total comp including RSUs. TD Bank AI and Scale AI Canada round out the major employers. TD's AI team operates with a mandate similar to Layer6 - applying ML to core banking - while Scale AI Canada (the federal supercluster, not the US data labeling company) funds AI projects across industries and employs ML engineers and researchers at partner organizations. Compare ML engineer salaries across all cities →

Toronto ML Engineer Salary by Experience Level

Junior ML Engineer (0-2 years experience) Entry-level ML engineers in Toronto earn CAD $90,000-$115,000 in base salary. This tier typically requires a strong university CS/statistics background or relevant bootcamp experience, with demonstrated ML project work. Cohere, Shopify, and bank AI teams hire at this level for roles focused on data pipelines, model evaluation, and feature engineering support. Mid-Level ML Engineer (3-5 years experience) Mid-level ML engineers earn CAD $130,000-$175,000 base salary, with top employers reaching CAD $190K including bonuses. At this level, engineers own model training pipelines, lead A/B test design, and contribute to production ML systems. Shopify's L5 equivalent, RBC Layer6's senior engineer band, and Cohere's mid-level band all cluster in this range. Senior ML Engineer (6-10 years experience) Senior ML engineers in Toronto earn CAD $160,000-$220,000 in base salary. At Google Brain Toronto and Cohere, total comp for this level (including equity) reaches CAD $240K-$280K. Senior engineers are expected to lead projects end-to-end: from research to production deployment, stakeholder alignment, and mentoring junior engineers. Staff / Principal ML Engineer (10+ years) Staff and principal ML engineers in Toronto earn CAD $220,000-$280,000+ in total compensation. This tier exists primarily at Cohere, Google/DeepMind Toronto, Shopify, and Vector Institute for named research leads. These roles require a track record of research publication or production systems with measurable business impact at scale.

CAD vs USD: The Context Every ML Engineer Needs

The most important financial reality for Toronto ML engineers is the CAD/USD exchange rate. At current rates (approximately 1 USD = 1.35 CAD), a senior ML engineer earning CAD $190K in Toronto is earning roughly USD $141K - compared to USD $200K-$280K for a Seattle counterpart at the same experience level.

This gap is real and significant. However, several factors moderate the comparison:

Canada's universal healthcare eliminates a cost that US employees cover through employer-sponsored plans or out-of-pocket. A US engineer on a $250K Seattle salary might pay $8K-$15K per year in health insurance premiums and out-of-pocket costs; a Toronto engineer pays nothing at point of care. Lower cost of living partially bridges the gap. Toronto housing costs have risen substantially, but a comparable condo near downtown Toronto runs CAD $3,200/month vs CAD $4,500+/month equivalent in Seattle. The effective purchasing power gap is smaller than the nominal salary gap. Ontario income tax is not a hidden advantage. Ontario's top marginal rate is approximately 13.16% on income over CAD $220,000, stacked on the federal rate. A Toronto ML engineer earning CAD $200K faces an effective combined tax rate (federal + Ontario) of approximately 40-43% - comparable to a California engineer. There is no provincial tax advantage equivalent to Washington State's 0% income tax in Seattle.

For ML engineers on the US side of the border considering a Toronto move, the honest calculus is: you will earn less in absolute terms. The draw is immigration access, research ecosystem quality, quality of life, and for many - the chance to work at Cohere or Vector Institute on problems that genuinely matter to the ML field globally.

Immigration and Express Entry: Toronto's Hidden Advantage

For ML engineers outside North America, Toronto has a structural advantage over US cities: Canada's Express Entry immigration system is significantly faster and more predictable than US H-1B lotteries.

Express Entry Comprehensive Ranking System (CRS) awards points for age, education, work experience, language proficiency (English and/or French), and a valid Canadian job offer. ML engineers with strong STEM backgrounds and a job offer from a Canadian employer typically score in the competitive range for invitation to apply. Provincial Nominee Programs (PNPs) - particularly Ontario's - provide additional pathways that can supplement Express Entry scores. Ontario has historically nominated tech workers with STEM backgrounds at above-average rates. LMIA-exempt work permits under the Global Talent Stream (GTS) allow Canadian employers to hire foreign ML engineers in as few as 2 weeks for certain high-demand roles. Cohere, Shopify, and large financial institutions are experienced GTS employers. This speed contrasts sharply with US H-1B processing, which involves a lottery and 1-3 year waits.

For an ML engineer in Europe, Asia, or South America choosing between Toronto and a US city, the immigration pathway is a genuine differentiator - especially for engineers who want permanent residency on a known timeline.

Key Skills for Toronto ML Engineers in 2026

Large Language Models and Applied NLP (highest demand) Cohere's growth has created a ripple effect across Toronto's employer base. Every major company hiring ML engineers in Toronto in 2026 is looking for LLM experience - fine-tuning, RAG architectures, prompt engineering at scale, and evaluation frameworks. Engineers with production LLM experience command 20-35% premiums over baseline. PyTorch and Model Development PyTorch is the dominant framework across Toronto's ML employer base. TensorFlow/Keras experience is increasingly secondary. Engineers applying to Cohere, Vector Institute, or Google Brain Toronto should expect to demonstrate PyTorch fluency in technical interviews. MLOps and Production ML Infrastructure ML engineers who can own the full pipeline - from model training to production deployment, monitoring, and retraining - are significantly more valuable than pure researchers. Kubeflow, MLflow, SageMaker, and Vertex AI experience are direct salary drivers. This skill set is particularly valued at Shopify and the bank AI teams. Reinforcement Learning and Sequential Decision Making A niche but premium skill set in Toronto's market, driven by applications in finance (Layer6, TD), autonomous systems, and game AI. Senior RL engineers can command top-of-range compensation at finance employers.

Toronto vs Other Canadian Cities and US Hubs

Toronto vs Vancouver Vancouver's ML market is smaller - Hootsuite, Slack's Canadian offices, and a cluster of gaming AI companies are the main employers. Base salaries run 10-15% below Toronto for equivalent roles, though British Columbia's cost of living is comparable. Toronto is the clear winner for ML career trajectory. Toronto vs Montreal Montreal is home to Mila (Yoshua Bengio's institute) and a vibrant academic ML scene. However, commercial ML employer density is lower, and salaries run 15-20% below Toronto. Montreal is excellent for ML researchers; Toronto is better for ML engineers who want production impact and higher compensation. Toronto vs Seattle Seattle offers 30-45% higher absolute compensation in USD terms. Washington's 0% state income tax further widens take-home pay. For ML engineers with US work authorization, Seattle is financially superior. Toronto's advantage is immigration accessibility and the specific research culture at Cohere and Vector Institute. Read our ML Engineer Salary in Seattle 2026 guide → Toronto vs San Francisco San Francisco pays the highest absolute ML salaries in North America - $150K-$350K+ depending on level. The California tax burden and cost of living erode some of the advantage, but even net of taxes, SF senior ML engineers out-earn Toronto counterparts significantly. Toronto competes on immigration, quality of life, and specific employer culture rather than compensation.

Negotiation Tips for Toronto ML Engineers

Anchor to USD-equivalent market data. When negotiating with Toronto employers - especially those with US counterparts like Google or Meta - present market data in USD and ask for CAD compensation that reflects parity. Sophisticated employers understand the conversion; using USD benchmarks shifts the conversation from "Canadian market norms" to "global talent competition." Quantify your LLM and MLOps experience precisely. Vague claims of "ML experience" compress offers into mid-range bands. Specific claims - "reduced model inference latency by 40% via quantization" or "built end-to-end RAG pipeline serving 2M requests/day" - justify above-band offers. Negotiate equity carefully at Canadian startups. Canadian startup equity is subject to complex tax treatment at exercise. Understand whether options are taxed at grant, vesting, or exercise, and model the actual after-tax value before comparing equity packages to US alternatives. Leverage competing offers across the border. Even if you are not actively pursuing US roles, a legitimate competing offer from a US employer (even a preliminary one) provides the most powerful negotiating leverage with Canadian employers. Toronto's ML employer base is aware that US companies are competing for the same talent.

Is Toronto the Right ML Market for You?

Toronto is the right choice for ML engineers who want to work on frontier AI problems in a world-class research ecosystem - specifically at Cohere, Google Brain Toronto, or Vector Institute - and who either cannot or prefer not to navigate US immigration. It is also an exceptional entry point for international ML engineers looking for a clear path to North American permanent residency.

The honest trade-off: Toronto pays 30-45% less than Seattle or New York in absolute USD terms. Ontario taxes are comparable to California. The CAD/USD gap is real and may not fully close during your career.

But for the right engineer - particularly those prioritizing immigration certainty, research culture, and the unique Canadian ML ecosystem anchored by Hinton's legacy and Cohere's ambition - Toronto offers something Seattle and San Francisco cannot replicate.

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Use CareerCheck's Toronto ML engineer salary tool to benchmark your current or target compensation against live market data - updated continuously with 2026 figures from Toronto's top ML employers.