AI/ML engineer salary in Canada

What Canadian companies post as base pay for AI and ML engineers, by city and level.

Key findings

• Senior AI/ML engineer base salary, Canada: $129K to $185K, median $162K
• Senior AI/ML engineer base salary, Toronto: $128K to $188K, median $155K
• Staff / lead AI/ML engineer base salary, Canada: $159K to $202K, median $199K

Salary ranges by city and level

A cell without enough postings shows "Not enough public data yet".

Level	Canada	Toronto	Vancouver	Montreal	Calgary	Ottawa	Winnipeg	Remote (Canada)
Junior (0–2 yrs)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)
Intermediate (2–5 yrs)	$115K to $187K, median $158K, n=23	$138K to $191K, median $172K. Higher than the level above. Small sample, n=18 from 10 employers.	Not enough posted ranges yet (n=2)	Not enough posted ranges yet (n=1)	Not enough posted ranges yet (n=2)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=8)
Senior (5–8 yrs)	$129K to $185K, median $162K, n=39	$128K to $188K, median $155K, n=20	$110K to $136K, median $135K, n=12	Not enough posted ranges yet (n=1)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=6)
Staff / Lead (8+ yrs)	$159K to $202K, median $199K, n=23	$158K to $202K, median $179K, n=18	Not enough posted ranges yet (n=1)	Not enough posted ranges yet (n=2)	Not enough posted ranges yet (n=1)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=0)	Not enough posted ranges yet (n=4)

Cite: Phil Mosher, Canadian tech salary data, baselinetalent.xyz, data as of October 29, 2026. CC BY 4.0.

All figures in CAD base salary.

Government survey figures

For AI and ML engineers across Canada, the government figures are: Job Bank median $96K, StatCan average offered wage $100K (Table 14-10-0444-01) for data scientists (NOC 21211). All levels, survey data, not job postings. They cover every level, so they sit below a senior posted range.

Skills that come up in hiring

There's no public pay data by skill yet, so read this section as hiring notes, not numbers. ML is too broad to hire for as one job. What you've deployed to production counts for more than what you've studied.

LLM engineering (RAG, LangChain, vector DBs)

Companies want people who have deployed retrieval-augmented generation (RAG) systems, built LLM features or run vector databases in production. "I've used GPT-4" and "I've built and deployed RAG systems" are very different lines on a resume.

Production ML deployments (MLOps, MLflow, BentoML)

Academia produces plenty of researchers. Far fewer people have shipped ML to production. Model serving, A/B testing for ML models, monitoring and retraining pipelines are where the real shortage is.

Deep learning frameworks (PyTorch, JAX, TensorFlow)

Knowing a framework doesn't set you apart anymore. Deep work on training performance, distributed training and custom ops still does.

Domain-specific AI (computer vision, NLP, RecSys)

Computer vision for robotics, NLP for legal tech and recommender systems for e-commerce each have their own patterns and constraints. Deep domain knowledge plus production ML skills is a rare mix.

What I'd do

AI/ML pay is the hardest of these roles to pin down. Postings for the same title range from research labs to banks adding a chatbot, and the work differs as much as the pay.

If you're hiring, be specific about the job. Say whether it's research, production ML or LLM features, and name the stack. Compete on the problems, the team and the pace of work, not only on cash.

If you're job hunting, be careful with equity. If you're taking below-market cash for equity, ask about the funding runway and real traction. And lead with your production ML experience. Plenty of people have studied ML. Fewer have shipped a model and kept it running.

Methodology

How every number here is built, tested and checked against government data: how the salary data is built.