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

ML engineer (payments) roles in Toronto typically pay USD 105,000 to USD 240,000 base salary in 2026, with total compensation often landing higher once bonus and equity are included. If you’re senior and working on fraud, risk, or real-time decisioning for a regulated payments stack, USD 180,000 to USD 300,000+ total comp is realistic.

Salary by Experience

Toronto salaries skew higher when the role sits inside fintech or a bank’s payments/risk team rather than a generic product ML team. The market also pays more for engineers who can handle both modeling and production systems.

What Affects Your Salary

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  Payments specialization matters

  • •Fraud detection, chargeback prevention, transaction risk scoring, and AML-adjacent ML work pay more than generic recommender or NLP roles.
  • •If you can explain model drift in a card authorization pipeline or tune precision/recall against fraud loss, you’re in the higher band.
• •

  Toronto’s industry mix creates a premium

  • •Toronto has a dense concentration of banks, credit unions, payments processors, and fintechs, so there’s strong demand for ML talent that understands regulated financial workflows.
  • •That concentration pushes up compensation for candidates who can work across data science, engineering, and compliance constraints.
• •

  Company type changes the ceiling

  • •Big banks usually offer lower base than top-tier fintechs or US-backed product companies with Toronto hubs.
  • •Banks may offset with better stability and pension/benefits; fintechs usually pay more cash/equity for the same scope.
• •

  Remote vs onsite affects offer structure

  • •Fully remote roles tied to US compensation bands can beat local Toronto packages.
  • •Hybrid or onsite roles often pay slightly less base but may come with stronger internal mobility and bonus structures.
• •

  Production ownership increases pay

  • •If you own feature stores, model deployment pipelines, monitoring dashboards, retraining triggers, and incident response for payment models, expect a premium.
  • •Pure notebook-based modeling without deployment responsibility usually lands lower.

How to Negotiate

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  Anchor on business impact, not model accuracy

  • •In payments, hiring managers care about fraud loss reduction, false-positive rate reduction at authorization time, approval lift, and latency.
  • •Bring numbers: “I reduced manual review volume by 18% while holding fraud capture flat” is stronger than “I improved AUC by 0.03.”
• •

  Price the risk you remove

  • •Payments teams deal with direct revenue leakage. If your work reduces chargebacks or prevents bad transactions at scale, quantify the dollars.
  • •That framing justifies asking for senior-level comp even if the title is mid-level.
• •

  Negotiate total comp separately from base

  • •Toronto offers often hide value in bonus and equity. Ask for the full breakdown: base salary, annual bonus target, sign-on bonus, stock vesting schedule.
  • •For fintechs with lighter base but meaningful equity upside, compare four-year value rather than monthly paycheck only.
• •

  Use market scarcity as leverage

  • •Candidates who understand both ML systems and payments domain knowledge are harder to replace than generalist ML engineers.
  • •If you’ve worked with card networks, PSPs, KYC/KYB signals, or real-time decision engines like Kafka + online features + low-latency inference APIs — say that clearly.

Comparable Roles

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  Machine Learning Engineer — Fintech

  • •Typical Toronto base: USD $130k–$220k
  • •Close match if the company builds lending, underwriting, or transaction intelligence products
• •

  Data Scientist — Fraud/Risk

  • •Typical Toronto base: USD $120k–$190k
  • •Usually less infrastructure ownership than an ML engineer role
• •

  Applied Scientist — Payments Risk

  • •Typical Toronto base: USD $150k–$230k
  • •Often heavier on experimentation and model research
• •

  Software Engineer — Platform/ML Infrastructure

  • •Typical Toronto base: USD $140k–$210k
  • •Pays well if you build serving layers, pipelines, or feature infrastructure for payment models
• •

  Quantitative Risk Analyst / Model Risk Specialist

  • •Typical Toronto base: USD $110k–$180k
  • •More common in banks; usually less coding depth than an ML engineer role

If you’re targeting maximum pay in Toronto for this niche in 2026, aim for teams working on fraud detection or transaction risk inside banks or fintechs. Those roles combine regulatory pressure with direct revenue impact — that combination is what moves compensation up fastest.

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.