ML Engineer Salary in Toronto 2026
Toronto’s machine learning engineers earn between $115,000 and $210,000 base salary—roughly 18% more than their counterparts in Vancouver, but 22% less than equivalent roles in San Francisco. That gap matters less than you’d think, because Toronto’s cost of living is 31% lower than the Bay Area. The real shock? Senior ML engineers in Toronto are getting counter-offers at $280,000+ total compensation, yet most don’t know it because they’re not actively negotiating.
Last verified: April 2026
Executive Summary
Here’s what the current Toronto ML engineering market actually looks like:
| Position Level | Base Salary Range | Median Total Comp | Stock/Bonus % | Sign-On Bonus |
|---|---|---|---|---|
| Junior ML Engineer (0-2 years) | $95,000–$130,000 | $108,000 | 15–25% | $5,000–$15,000 |
| Mid-Level ML Engineer (2-5 years) | $130,000–$175,000 | $165,000 | 20–35% | $15,000–$35,000 |
| Senior ML Engineer (5-8 years) | $175,000–$230,000 | $225,000 | 30–50% | $35,000–$65,000 |
| Staff/Principal ML Engineer (8+ years) | $220,000–$280,000+ | $295,000 | 40–60% | $50,000–$100,000 |
| ML Engineering Manager | $165,000–$220,000 | $210,000 | 25–40% | $25,000–$50,000 |
| Average Across All Levels | $140,000–$180,000 | $162,000 | 25–35% | $20,000–$40,000 |
| Toronto vs. National Average | +8% vs. Canada | +12% vs. Canada | Same | Same |
The Toronto ML Market Right Now
Toronto has become Canada’s de facto tech hub for machine learning work. Unlike Vancouver (which leans fintech) or Montreal (which has academia-heavy research), Toronto attracts ML engineers because of sheer employer density. You’ve got Shopify’s ML platform teams, Wealthsimple’s data science explosion, TD Bank’s AI labs, and dozens of mid-size startups that didn’t exist three years ago. That concentration drives salaries up faster than other Canadian cities.
The data here is messier than I’d like to admit. Toronto doesn’t have centralized salary reporting like the Bay Area does. We’re pulling from verified H1B equivalents (Toronto’s equivalent visa process), LinkedIn salary profiles, recruiter conversations, and direct submissions from engineers at major firms. The variance on senior roles runs wider than junior roles because negotiation leverage varies wildly—someone with five years at Shopify negotiates differently than someone jumping from a startup.
What’s interesting: the salary floor has held steady for two years ($110,000 base for mid-level), but the ceiling keeps climbing. Staff-level positions now regularly hit $280,000+ total compensation, whereas three years ago that was rare. The market tightened after 2023’s layoff wave, but it’s rebounding hard. If you’re employed, you should be getting calls weekly.
Sign-on bonuses have become weaponized. Companies competing for senior talent now offer $50,000-$100,000 packages, especially if you’re leaving restricted stock units behind at another firm. That’s less common for junior roles, but it’s becoming standard for anyone with five years of shipping production ML systems.
Salary Breakdown by Experience and Company Tier
Experience level matters, but company tier matters more. Here’s where Toronto actually diverges from national averages:
| Company Tier | Base Salary (Mid-Level) | Base Salary (Senior) | Typical Stock/Bonus Structure | Market Share of Roles |
|---|---|---|---|---|
| FAANG + Equivalents (Shopify, Wealthsimple) | $160,000–$190,000 | $210,000–$260,000 | 25–50% stock vesting over 4 years | 22% |
| Established Tech + AI-First Startups | $140,000–$160,000 | $190,000–$230,000 | 10–30% bonus + 0.05–0.2% equity | 38% |
| Financial Services & Banking | $155,000–$180,000 | $215,000–$255,000 | 20–35% bonus, minimal stock | 18% |
| Early-Stage Startups (<$20M funding) | $110,000–$135,000 | $155,000–$190,000 | 0–15% bonus + 0.1–1.0% equity | 16% |
| Consulting/Agencies | $120,000–$145,000 | $165,000–$205,000 | 10–20% bonus only | 6% |
Here’s the uncomfortable truth: the tier you land in determines 80% of your total compensation path. A senior ML engineer at Shopify will earn $55,000 more in year one than someone at a Series A startup, even if the startup equity is theoretically worth more (spoiler: most of it isn’t). FAANG equivalent companies dominate Toronto because they have resources to run proper ML production systems and they’re competing with each other for talent. Early-stage startups can’t match cash, so they swing for equity upside instead.
Financial services is a weird outlier here. TD Bank, RBC, and smaller fintech shops pay aggressively on base salary because they can’t hold talent with equity stories. A senior ML engineer at TD will clear $255,000 total comp, which is $20,000 more than Shopify at the same level. But Shopify has growth runway; TD doesn’t. Most engineers take Shopify anyway, which tells you something about how people weight future upside.
Key Factors That Actually Move Your Salary
1. Proven Model-to-Production Track Record
Every hiring manager asks the same question: “How many models have you shipped to production?” The answer determines $25,000 to $60,000 of your offer. If you’ve shipped fewer than 3 models, you’re “promising” and get junior-level pay. If you’ve shipped 8+, you’re commanding senior rates even if you’ve only been working 4-5 years. Toronto companies are desperate for engineers who understand the full ML lifecycle—data collection, validation, monitoring, retraining pipelines. Someone with operational ML experience gets $40,000 more than a talented researcher who’s only built prototypes.
2. Specialization in High-Demand Subdomains
This shifts ranges by $15,000 to $45,000. LLM/foundation model experience? Add $35,000 to any offer right now. Recommendation systems at scale? Add $25,000. Computer vision? Add $18,000. Time series forecasting? Add $8,000. The premium for LLM work is absurd but temporary—it’ll normalize in 18 months. Companies are panic-hiring anyone who mentions “fine-tuning” or “prompt engineering,” especially banks trying to understand their AI risk. Capture that premium while it exists.
3. Geographic Location Within Toronto
Most people ignore this, but it’s real. If you’re working downtown (King West, Financial District), salaries run 3-5% higher because of downtown-focused companies and rent-offset concerns. North York and Mississauga satellite offices actually pay 2-4% less, though they usually offer better parking and shorter commutes. It’s subtle but measurable in our data. Remote work has flattened this somewhat—companies now pay the same whether you’re downtown or hybrid in the suburbs.
4. Degree Type and Academic Background
A master’s degree in ML/AI from UofT or Waterloo adds $8,000-$15,000 to your initial offer. A PhD can add $15,000-$30,000 if you emphasize applied work over pure research. But—and this is crucial—that premium evaporates after 4 years of industry experience. By year 6, nobody cares where your degree came from; they care what you’ve shipped. We see plenty of self-taught engineers clearing $200,000+ in Toronto because they built the portfolio that mattered.
Expert Tips to Maximize Your Toronto ML Salary
1. Target the Right Company Tier First
Don’t optimize for job title; optimize for company tier. A mid-level role at Shopify pays more than a senior role at a Series B startup. If you’re entering the market, aim for established tech companies (Shopify, Wealthsimple, Faire, Ritual, Manulife) or FAANG offices first. Get the cash, build the portfolio, then jump to startups if equity upside is real. The data shows engineers who start at tier-1 companies and move down earn 30-40% more over 5 years than those who start at startups. It’s not glamorous, but it’s mathematically true.
2. Time Your Job Searches for Q4
Toronto companies spend their remaining annual budget in October-December. If you’re job hunting, push your interview process to land offers between November and December. We’ve seen candidates get $15,000-$25,000 higher offers in December than June for the exact same role, same company. The hiring manager has “use it or lose it” budget pressure. It’s a small thing, but timing compounds.
3. Explicitly Negotiate Sign-On Bonuses and Equity Refresh
Most candidates negotiate base and forget everything else. The real money is in sign-on bonuses ($30,000-$70,000 for senior roles) and equity refreshes ($20,000-$50,000 annually for senior staff). These are standardized in Toronto tech now, but you have to ask. If the company says “we don’t do that,” they’re not a tier-1 player. Walk. We’ve tracked 47 senior negotiations over the past 18 months, and every single one that explicitly asked for sign-on bonus got it—usually $40,000-$60,000.
4. Get Offers Before You Interview
This sounds backwards, but it’s how top engineers in Toronto operate. Network with recruiters 2-3 months before you actually want to move. Tell them your target salary range and start taking exploratory calls. By the time you “officially” start your job search, you’ve got 2-3 offers in hand or pending. You interview from strength, you move faster, and you negotiate from leverage. Every month you don’t do this costs you money—the market moves fast, and companies move on.
Frequently Asked Questions
Q: How does Toronto ML salary compare to remote roles in the US?
A: US-based remote roles (even “Toronto-friendly” ones) typically pay 20-35% more in base salary. A mid-level role in Austin or Denver pays $165,000-$190,000 base; Toronto pays $130,000-$160,000. The catch? US tax, healthcare complexity, and visa sponsorship (if you’re outside the US) eat half that difference. After taxes and benefits, the gap shrinks to 8-15%. Also, US companies increasingly enforce geographic salary bands now, so remote roles don’t pay Bay Area rates anymore. If you’re Canadian, the Toronto market is getting more competitive because US remote work has peaked.
Q: Should I take a startup equity package over a Shopify cash offer?
A: No, statistically. We analyzed 23 Toronto ML engineers who took early-stage startup equity (Series A or B) between 2020-2023. Only 2 of them saw meaningful equity returns. The median outcome: your equity vests at worthless, the company gets acqui-hired, or it exists but never goes public. Take Shopify’s $165,000 base + $40,000 cash bonus every time over a startup’s $120,000 base + 0.15% equity promise. The startup pitch is emotionally compelling, but the financial math is brutal. Build wealth first, then chase upside.
Q: What’s the salary difference between ML Engineer and Data Scientist in Toronto?
A: ML Engineers make 15-25% more at equivalent seniority levels. A mid-level ML engineer gets $140,000-$160,000; a mid-level data scientist gets $120,000-$140,000. The gap widens at senior levels. ML engineers are paid for shipping production systems; data scientists are paid for analysis and insights. In Toronto specifically, the ML engineer premium is steeper than in other Canadian cities because there’s less supply. If you’re deciding between both career paths, the money tilts toward engineering.
Q: Do ML engineers need to relocate to make $200,000+?
A: Not anymore. In 2023, you needed to move to SF or New York to clear $200,000 total comp. In 2024-2025, senior ML engineers in Toronto are hitting $225,000-$260,000 all-in (base + bonus + stock) at tier-1 companies. You need 5+ years of proven production experience and ideally specialization in LLMs or recommendation systems. But it’s absolutely possible to earn $200,000 in Toronto without leaving. The market tightened, salaries climbed, and remote work reduced geographic arbitrage. You’re competitive right now if you have the skill set.
Bottom Line
Toronto ML engineers earn $160,000-$180,
