Hiring an ML/MLOps Engineer in Montreal in 2026

If you are trying to hire a machine learning engineer in Montreal in 2026, you are going after the most contested profile in the Canadian tech market. The good news: you are in the right place, one of the world's AI capitals. The less good news: every company on the planet knows it too. LinkedIn's 2026 Jobs on the Rise report ranks AI Engineer as the fastest-growing job, with postings up 143% year over year (source: Dice). Here is our guide to hiring an ML or MLOps engineer in Montreal: role definitions, sourced salaries, skills to assess and strategies that actually work.

Montreal, a global artificial intelligence hub

Montreal holds a unique position in the global AI ecosystem. The city is home to Mila, the Quebec AI institute founded by Turing Award laureate Yoshua Bengio: it is the world's largest academic research institute in deep learning, with a community of more than 1,400 researchers (source: Mila). Around Mila orbit the labs of Google DeepMind, Microsoft and Meta, along with a dense fabric of AI startups.

The talent pool follows: Greater Montreal counts about 27,000 workers with AI skills, up 15% over twelve months, within an ecosystem of more than 160,000 tech workers (source: Montréal International). For an employer, that means rare access to profiles trained at the leading edge of research. But it also means fierce competition, including from US employers hiring remotely. We mapped this market in full in our article on recruiting AI talent in Canada.

ML Engineer, Data Scientist, MLOps: who does what?

Many searches fail because the role is poorly defined from the start. These three roles overlap but are not interchangeable:

• The Data Scientist explores data, formulates hypotheses, builds model prototypes and produces analyses. Typical deliverable: a notebook, a study, an experimental model. Strong in statistics and experimentation.
• The Machine Learning Engineer turns those prototypes into production systems: industrializing training, optimizing performance, integrating the model into the product. A software engineer first, specialized in machine learning.
• The MLOps Engineer builds and operates the infrastructure that keeps models alive: training and deployment pipelines, model and data versioning, drift monitoring, reproducibility. The DevOps counterpart of the AI world.

In small teams, one person often covers two of these roles. But post the right title: an MLOps engineer will not answer a Data Scientist posting, and vice versa. Note also that these roles rest on solid data foundations; if your data pipelines are fragile, you may want to start by hiring a data engineer in Montreal.

ML and MLOps engineer salaries in Montreal in 2026

These profiles command a clear premium over classic software development. The 2026 benchmarks for Montreal:

• Average machine learning engineer salary: about $142,500 per year, with most salaries between $112,700 (25th percentile) and $165,500 (75th percentile), and a 90th percentile near $195,000 (source: ZipRecruiter).
• Indeed puts the Montreal average at about $144,700 per year (source: Indeed).
• According to ERI data, the average sits around $143,000, with entry-level profiles (1 to 3 years) earning around $93,600 and highly experienced profiles (8+ years) around $166,000 (source: ERI).

In practice, budget $110,000 to $160,000 for most hires, and more for senior profiles with large-scale production experience or large language model expertise. To compare with other tech roles, read our article on tech salaries in Montreal in 2026.

The skills to assess (and how to assess them)

A PhD is no guarantee of the ability to put a model in production, and an excellent DevOps engineer cannot improvise model evaluation. Here is what we check:

• ML foundations: command of PyTorch (the de facto standard, and one of the most common skills among AI engineers according to LinkedIn), understanding of bias-variance trade-offs, ability to choose an evaluation metric relevant to the business problem.
• Model deployment: containerization (Docker, Kubernetes), inference APIs, latency and cost optimization, load testing. A simple, revealing question: "Walk me through shipping a model to production, from training to monitoring."
• MLOps pipelines and tooling: orchestration (Airflow, Kubeflow or equivalent), experiment tracking (MLflow, Weights & Biases), data versioning, model drift detection in production.
• LLMOps: more and more mandates involve large language models: retrieval-augmented generation (RAG), evaluating non-deterministic outputs, managing inference costs, guardrails. LinkedIn indeed identifies LangChain, RAG and PyTorch among the most common AI engineer skills (source: Dice).
• Core software engineering: clean code, code review, testing. A model in production is still software.

Recommended assessment format: an in-depth technical discussion of a real project from the candidate, plus a short paid practical exercise or code review, rather than theoretical algorithm tests unrelated to the job.

Extreme scarcity, exploding timelines

Demand structurally exceeds supply. According to LinkedIn data analyzed by the World Economic Forum, AI has already created 1.3 million new jobs, with AI engineer roles at the forefront (source: World Economic Forum). The concrete result for employers: for AI/ML specialists, time to fill reaches 89 days, nearly three months for a single hire, because the pool of engineers with production LLM pipeline experience remains tiny relative to demand (source: KORE1).

Concretely, a good Montreal ML engineer who starts looking receives several simultaneous offers, often sweetened by remote US employers. Every day of delay in your process increases the odds of losing them.

Recruiting strategies that work for these profiles

• Sell the problem, not the job: these engineers choose challenges (unique data, scale, measurable impact). Your posting must describe the ML problem to solve, the data volume and the stack.
• Post the salary and align it with the data above: a range below $110,000 for an experienced profile disqualifies your offer from the start.
• Compress your process: three steps maximum, decision within two weeks. Against a market timeline of 89 days, speed is your first competitive advantage.
• Tap the local ecosystem: internships and partnerships with Mila and the universities, presence at Montreal AI events, technical publications from your team.
• Broaden intelligently: an excellent backend software engineer with solid data foundations can become an MLOps engineer within months; hiring for potential costs less than outbidding everyone on the same ten profiles.
• Use specialized headhunting: most of these profiles are not actively looking and will never see your posting.

Hire your ML engineer with VALO

At VALO Recrutement, we recruit ML, MLOps and data profiles for companies in Montreal and across Quebec, drawing on our deep knowledge of the local AI ecosystem. We present your first qualified candidates in under 2 weeks, with transparent fees of 18% of annual salary and a 3-month replacement guarantee. Before launching your search, download our tech recruiting guide, then discover our services for employers. Your next ML engineer is probably already employed somewhere in Montreal: we know where to find them.