ML engineer (fintech) Salary in Toronto (2026): Complete Guide

ML engineer (fintech) salaries in Toronto in 2026 typically land between $95,000 and $240,000 USD base depending on seniority, with total comp often pushing higher when bonus and equity are included. For strong fintech ML candidates, the real negotiation band is usually $130,000 to $190,000 USD once you get past entry level.

Salary by Experience

A few notes on these numbers:

• •Toronto fintech usually pays above generic software roles because the work touches fraud loss, credit risk, AML/KYC automation, and revenue-critical decisioning.
• •Principal-level roles can go well beyond the table if the company is a top-tier bank-backed platform or a well-funded payments/credit startup.
• •If you’re comparing offers across Canada and the US remotely from Toronto, anchor on base + bonus + equity, not just salary.

What Affects Your Salary

• •

  Domain specialization matters.
  ML engineers who have shipped fraud detection, risk scoring, recommendation systems for financial products, or document intelligence for underwriting get paid more than generalist ML engineers.

• •

  Fintech pays a premium over generic tech in Toronto.
  Toronto is Canada’s banking and financial services center. That creates consistent demand from banks, insurers with digital arms, payments companies, lending platforms, and wealth-tech firms.

• •

  Regulated environments increase comp.
  If you can work with model governance, explainability, audit trails, PII controls, and compliance-heavy pipelines, your market value rises fast. Most teams need people who can ship models without creating regulatory problems.

• •

  Production MLOps experience moves the number.
  Training models is not enough. Engineers who can own feature stores, CI/CD for models, monitoring drift, retraining triggers, and latency-sensitive inference usually sit at the top of the band.

• •

  Remote vs onsite changes leverage.
  Fully remote roles tied to US budgets often pay more than local-only Toronto roles. Onsite or hybrid roles at legacy institutions can be lower on base but sometimes make up part of it with stability and bonus structure.

How to Negotiate

• •

  Anchor on business impact.
  Don’t lead with “I know ML.” Lead with outcomes: reduced fraud losses by X%, improved approval rates without increasing default risk, cut manual review volume by Y%, or improved model latency by Z ms.

• •

  Price yourself against risk-adjusted value.
  In fintech, a model that improves conversion but increases losses is not a win. Show that you understand both sides of the metric: revenue lift and downside protection.

• •

  Ask about total comp structure early.
  Toronto fintech offers can vary widely on bonus target and equity quality. Ask for base salary range first, then clarify annual bonus %, sign-on bonus availability, and whether equity is meaningful or just paper value.

• •

  Use competing benchmarks from adjacent roles.
  If you’re interviewing for a role that includes data engineering or platform ownership on top of ML work, compare it against senior backend or applied scientist compensation bands. Hybrid scope should not be priced like a pure ML support role.

Comparable Roles

• •

  Applied Scientist — $140,000 to $230,000 USD base
  Usually closer to research-heavy ML work with stronger emphasis on experimentation and modeling depth.

• •

  Data Scientist (Fintech) — $110,000 to $180,000 USD base
  Often pays slightly less unless the role is very close to product decisioning or risk modeling.

• •

  ML Platform Engineer — $135,000 to $215,000 USD base
  Strong comp if you own deployment infrastructure, inference systems, or feature pipelines for multiple teams.

• •

  Risk Modeling Engineer — $130,000 to $210,000 USD base
  Common in lending and banking; compensation rises when the role directly affects credit policy or capital allocation.

• •

  Fraud Detection Engineer — $140,000 to $220,000 USD base
  High-value niche in payments and neobanks because fraud losses are immediate and measurable.

If you’re negotiating in Toronto’s fintech market in 2026: optimize for roles where your models touch money directly. That’s where ML engineer compensation stops looking like standard software engineering and starts reflecting actual business risk.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.