Machine learning engineering has become one of the most in-demand technical disciplines in Canada, with a job market that expanded rapidly through 2024 and 2025 and shows no signs of cooling in 2026. Whether you are an applied scientist looking to move into production work, an ML engineer targeting senior roles at a Canadian AI lab, or an LLM engineer riding the generative AI wave, the opportunities across Toronto, Montreal, and Vancouver are concrete and growing. This guide walks you through the landscape, compensation benchmarks, employer highlights, and how to position your application for success.
Quick Takeaways
- ML engineering, applied scientist, and LLM engineer roles fall under NOC 21234 in Canada's occupational classification system.
- Total compensation at scale-ups like Cohere, Tenstorrent, RBC Borealis, and ServiceNow ranges from roughly $160,000 to $260,000 CAD annually for senior candidates in 2026.
- Affiliations with Mila (Montreal), Vector Institute (Toronto), or Amii (Edmonton) give your application a measurable credibility boost with Canadian hiring managers.
- International candidates may qualify for Canada's Global Talent Stream, which can process work permits in as little as two weeks.
- You can browse current machine learning jobs in Canada on the TechEmployment.ca job seekers page.
What Machine Learning Jobs in Canada Look Like in 2026
The Canadian AI sector has matured considerably. Early-stage startups from the 2019-2021 cohort are now mid-sized companies running production systems at scale. Large banks, insurers, and telecoms have moved beyond pilot projects and are hiring teams to maintain and extend deployed models. Meanwhile, the arrival of well-funded foundation model companies has created a new tier of roles focused entirely on large-scale pretraining, fine-tuning, and inference optimization.
The Three Core Role Types
ML Engineer. This role sits at the intersection of software engineering and model development. You are responsible for building training pipelines, writing efficient data loaders, managing experiment tracking, and deploying models to production serving infrastructure. Strong Python, familiarity with distributed training frameworks like PyTorch and JAX, and experience with cloud platforms (AWS, GCP, or Azure) are the baseline expectations at most Canadian employers.
Applied Scientist. This title is common at larger organizations. The applied scientist focuses on research that has a near-term product connection. You run experiments, write papers when warranted, and collaborate closely with engineers to push findings into production. A graduate degree (MSc or PhD) in machine learning, statistics, or a related field is the norm, though strong self-taught candidates with a published track record do break in.
LLM Engineer. This role reflects the shift toward large language models that accelerated through 2024 and 2025. LLM engineers work on fine-tuning, RLHF pipelines, retrieval-augmented generation (RAG) systems, and inference serving for generative AI products. The role blends engineering discipline with a working understanding of how language models behave, where they fail, and how to evaluate them reliably at production scale.
NOC 21234 and What It Means for Your Job Search
Canada's occupational classification system groups most machine learning roles under NOC 21234 (Data scientists and statisticians) or NOC 21231 (Software engineers and designers) depending on how the employer frames the specific duties. If your role is primarily model development and research, you will likely be classified under 21234. This distinction matters for immigration purposes and for understanding how your role is benchmarked against federal salary and credential standards in programs like the Global Talent Stream.
Canadian Employers Worth Targeting
The Canadian ML hiring landscape spans foundation model companies, research labs embedded inside large financial institutions, and established enterprise software companies with significant AI investment. Here are the employers most active in the Canadian ML community right now.
AI Labs and Foundation Model Companies
Cohere (Toronto) is one of Canada's most prominent foundation model companies. Cohere builds large language models for enterprise use and hires ML engineers, research scientists, and LLM-focused engineers at all seniority levels. The company expanded its engineering team significantly in recent years. Roles here are competitive and typically require a strong publication record or demonstrable open-source contributions for research-track positions.
Tenstorrent (Toronto) designs AI hardware and the software stack that runs on it. If you have experience with compiler optimization, kernel writing, or low-level ML system work, Tenstorrent is worth targeting. The company's work on hardware-software co-design creates cross-functional roles that blend systems programming with ML at the infrastructure level.
RBC Borealis is the Royal Bank of Canada's AI research lab. Borealis publishes research, collaborates with Mila and Vector, and employs applied researchers who work on problems relevant to financial services, including risk modeling, fraud detection, and natural language processing. For candidates who want the intellectual environment of an AI lab with the stability of a major financial institution, this is a strong option.
ServiceNow has a substantial AI and ML team in Montreal. The Montreal office works on natural language processing, workflow intelligence, and automation features for the ServiceNow platform. It is one of the larger non-startup ML employers in Quebec and regularly hires for tech jobs montreal at both senior and mid-level.
Banks, Telecoms, and Established Employers
Beyond the labs, Canada's major banks (TD, Scotiabank, BMO) and telecoms (BCE, Telus, Rogers) all maintain ML teams. Base compensation tends to be lower than the lab tier, but the work is stable, the data sets are large, and these employers often have strong internal mobility programs. They are also more likely to offer structured onboarding for candidates who are new to Canada.
Compensation Benchmarks for 2026
Salaries for machine learning roles in Canada have risen considerably over the past few years. What follows is a qualitative range based on what employers have published publicly and what candidates have reported in community forums. Treat these as directional guidance, not guarantees.
Entry to Mid-Level (0-4 Years of ML Experience)
At this level, total compensation (base salary plus equity where applicable) tends to fall between $110,000 and $160,000 CAD at well-funded startups and established tech companies. Large banks may offer lower base salaries but stronger benefits and pension contributions that partially offset the gap, particularly relevant if you are building long-term financial stability in Canada.
Senior Level (5+ Years)
Senior ML engineers and applied scientists at scale-ups such as Cohere, ServiceNow, or RBC Borealis can realistically target total compensation in the $160,000 to $260,000 CAD range. Equity grants, RSUs, and performance bonuses make the upper end of this range achievable at well-funded private companies, though equity in private companies carries liquidity risk that is worth factoring into your evaluation.
Staff and Principal Roles
At the most senior individual contributor levels, compensation at the top Canadian employers can exceed $300,000 CAD in total comp. These roles are rare and highly competitive. If you are targeting them, your application will need to demonstrate independent research impact or a track record of leading large technical programs with measurable outcomes.
The Mila, Vector, and Amii Advantage
Canada's three national AI institutes are not just research organizations. They function as talent pipelines, community hubs, and credibility signals that hiring managers at Canadian ML companies recognize and respect immediately.
Mila: Montreal's Academic ML Powerhouse
Mila is the Quebec AI Institute, co-founded by Yoshua Bengio. It is one of the largest academic ML research groups in the world. A Mila affiliation (as a student, intern, or visiting researcher) signals to employers that you have worked in a rigorous research environment alongside people who are actively advancing the field. Many Mila alumni move directly into senior roles at Cohere, Google DeepMind Montreal, and RBC Borealis.
Vector Institute: Toronto's Industry Fellowship Network
The Vector Institute in Toronto focuses on applied machine learning and deep learning research with a strong industry partnership model. Vector runs a fellowship program that connects graduate students and postdoctoral researchers with industry partners. If you are entering the market from a Canadian university and have the option to apply for a Vector fellowship or internship, pursue it. The professional network access alone accelerates your job search in a meaningful way.
Amii: The Alberta Pipeline
The Alberta Machine Intelligence Institute (Amii) is based in Edmonton and affiliated with the University of Alberta, which has one of the longest-running reinforcement learning research programs in the world. Edmonton has a smaller ML job market than Toronto or Montreal, but Amii graduates regularly land roles at major employers across Canada and internationally. An Amii affiliation carries weight well beyond the province.
Even if you did not come up through one of these institutes, engaging with their public events, workshops, and online communities helps you build connections inside the Canadian ML ecosystem. Many employers post roles to institute job boards before listing them on general job platforms.
Global Talent Stream: A Fast Path for International Candidates
If you are an ML engineer or applied scientist outside Canada researching data scientist jobs canada or tech roles here, Canada's Global Talent Stream (GTS) under the Temporary Foreign Worker Program is one of the fastest legal pathways to Canadian work authorization. The GTS is designed specifically for high-demand tech roles and can process applications in as little as two weeks when the employer files under Category B.
Which Roles Qualify
NOC 21234 (Data scientists and statisticians) appears on the Global Talent Occupations List, which means ML roles classified under this NOC are eligible for the accelerated GTS processing timeline. Your prospective employer must be a designated GTS employer and must initiate the application on your behalf. The employer applies, not you.
What This Means for Your Application
If you are applying from outside Canada, it is worth noting in your communication with recruiters that you are aware of the GTS process. Some smaller employers have not used it before and may not know it exists. Demonstrating that knowledge can reduce friction early in the hiring process. That said, this guide does not constitute immigration advice. Consult a regulated Canadian immigration consultant or lawyer for guidance specific to your situation.
You can find GTS-eligible tech roles across Canada, including tech jobs montreal and Toronto openings, listed on TechEmployment.ca, which aggregates openings from employers that hire both domestic and international candidates.
How to Build a Stronger Application
Your Resume and Portfolio
For ML roles, your GitHub profile, Hugging Face repositories, or published papers function as a portfolio in a way that a resume alone cannot. Employers at Cohere, RBC Borealis, and similar organizations will look at your public work before your interview. Make sure your repositories are documented and that your most relevant projects are pinned and described in plain language.
On your resume, lead with technical specifics: model types you have trained (transformer, diffusion, RLHF), frameworks (PyTorch, JAX, TensorFlow), and scale where relevant (parameter counts, dataset sizes, compute budgets). Vague claims undermine your application. A line saying only that you 'improved model accuracy' is weak. Stating that you 'reduced validation loss by 12% on a 7B parameter LLM through architectural ablations' is specific and gives interviewers something concrete to probe.
Interview Preparation
Machine learning interviews in Canada typically include a coding round (usually medium-difficulty Python problems), a system design round focused on ML infrastructure (feature stores, training pipelines, serving architecture), and an ML theory round covering topics like regularization, optimization, and evaluation metrics. Some employers add a research discussion where you walk through a paper or your own published work in detail.
Practice explaining your past projects clearly and concisely. The ability to describe what you built, why you made specific technical decisions, and what the measurable impact was is a skill that separates strong candidates from strong engineers. Interviewers at labs like RBC Borealis and Cohere are especially attentive to how clearly you can communicate technical tradeoffs.
Networking in the Canadian ML Community
In-person and hybrid events at Mila, Vector, and Amii are among the most efficient ways to meet people at target employers. NeurIPS Canada workshops, the Canadian Conference on Artificial Intelligence (CAIAC), and various meetups in Toronto and Montreal attract both industry practitioners and researchers. If you can get your work in front of the right people informally before applying, your application arrives with context rather than cold.
FAQ
What is the NOC code for machine learning jobs in Canada?
Most machine learning roles in Canada fall under NOC 21234 (Data scientists and statisticians) or occasionally NOC 21231 (Software engineers and designers) depending on the specific job duties. If your employer is sponsoring your work permit or you are applying for immigration-related programs, confirm which NOC code applies to your specific role with your employer's HR team or a regulated immigration consultant.
Is a PhD required to work in ML in Canada?
No. A PhD is expected for research-track positions at places like Mila or RBC Borealis, and it is a meaningful advantage for applied scientist roles. However, ML engineer and LLM engineer positions at startups and established tech companies regularly hire strong candidates with MSc degrees or even undergraduate degrees paired with demonstrated project work, open-source contributions, or relevant publications.
What cities have the most machine learning jobs in Canada?
Toronto and Montreal are the two largest markets. Toronto has a dense cluster of fintech, healthtech, and AI companies plus the Vector Institute. Montreal has Mila, ServiceNow's AI team, and a strong presence from the gaming and aerospace sectors that use applied ML. Vancouver is a smaller but growing market, particularly for candidates interested in roles at gaming companies or the Canadian offices of US-headquartered tech firms.
How does the Global Talent Stream help international ML candidates?
The Global Talent Stream allows eligible employers to sponsor work permits for high-demand tech occupations including roles under NOC 21234. The processing target is two weeks under Category B, compared to the standard multi-month timeline. The employer must initiate the process. If you are applying from outside Canada, raise GTS eligibility with your recruiter early so the employer can assess whether they qualify as a designated participant.
What skills are most in demand for ML roles in Canada right now?
In 2026, employers are prioritizing candidates with hands-on experience in large language model fine-tuning and deployment, distributed training on GPU clusters, inference optimization techniques (quantization, speculative decoding, KV cache management), and evaluation frameworks for generative AI systems. Strong foundations in PyTorch, Python, and cloud infrastructure remain baseline requirements across all seniority levels.
How do I find machine learning jobs in Canada actively?
Institute job boards (Vector, Mila, Amii), LinkedIn, and company career pages are the primary sources for ML roles in Canada. General tech job boards that aggregate Canadian openings are also useful for monitoring hiring volume across employers. The TechEmployment.ca job seekers page lists tech and ML openings across Canada, and you can create a candidate profile to receive alerts for roles that match your background.
Machine learning roles in Canada are attainable in 2026 if your skills are current and your application materials reflect genuine technical depth. The combination of well-funded domestic employers, a strong academic ecosystem through Mila, Vector, and Amii, and immigration pathways like the Global Talent Stream makes Canada one of the more accessible ML job markets globally for both domestic and international candidates. Build your public portfolio, engage with the community, and apply through channels that match your target companies. Ready to take the next step? Visit TechEmployment.ca at https://techemployment.ca/job-seekers to browse current openings and create a candidate profile.