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Machine Learning Engineer Jobs in Montreal, QC (NOW HIRING)

... Developer Position overview As a Senior ML Developer on the team, you will be responsible for ... Design and implement Machine Learning capabilities that improve Autodesk's RAG platforms * Perform ...

We are seeking a senior machine learning (ML) research developer to join our team working on a novel AI safety agenda. In this role, you will work closely with ML research scientists to solve ...

Design, develop, and validate machine learning models, predictive models, and advanced analytical solutions * Perform exploratory data analysis, feature engineering, and model performance evaluation

Lead AI/ML Engineer

Montreal, QC · On-site

CA$130K - CA$160K/yr

Partner with Data Science, ML, and Backend teams to productionize machine learning features in ... Experience managing small data engineering teams and setting technical direction * Strong ownership ...

D. degree in Artificial Intelligence, Machine Learning, Data Science, or a related field. * You ... You enjoy and are highly proficient in Python programming (knowledge of C++ is considered an asset)

Position Summary The Process Engineer will participate in projects and process improvement ... and machine learning initiatives. Perform other related duties as assigned. Management ...

... analytics, engineering, product) on high-impact end-to-end use cases (anomaly detection ... Utilize machine learning and advanced statistical methods to identify trends and patterns in ...

Showing results 41-60

Machine Learning Engineer information

See Montreal, QC salary details

$64.3K

$142.6K

$217.9K

How much do machine learning engineer jobs pay per year?

As of Aug 15, 2026, the average yearly pay for machine learning engineer in Montreal, QC is $142,553.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,681.00 and $165,532.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

Infographic showing various Machine Learning Engineer job openings in Montreal, QC as of August 2026, with employment types broken down into 96% Full Time, 2% Temporary, and 2% Contract. Highlights an 70% In-person, 7% Hybrid, and 23% Remote job distribution, with an average salary of $142,553 per year, or $68.5 per hour.

Développeur en apprentissage automatique III / Machine Learning Developer III

Trane Technologies

Montreal, QC

Full-time

Re-posted 24 days ago


Trane Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 299 frontline employees who took The Breakroom Quiz

117th of 540 rated manufacturers


Job description

Be a part of our mission! As a world leader in creating comfortable, sustainable, and efficient climate solutions for buildings, homes and transportation, it's our responsibility to put the planet first. For us at Trane Technologies, and through our businesses including Trane® and Thermo King,  sustainability is not just how we do business—it is our business.  Do you dare to look at the world's challenges and see impactful possibilities?  Do you want to contribute to making a better future?  If the answer is yes, we invite you to consider joining us in boldly challenging what's possible for a sustainable world.

Learn about our benefits designed for you to Thrive at work and at home. 

We boldly go.

Where is the work:

Our BrainBox AI Workplace Presence model dedicates specific in-office days each month to focus on relationships, learning and innovation.

Ce que vous ferez :

  • Mise en œuvre de cadres : Participer activement à la conception et à l’implantation concrète de cadres d’IA évolutifs adaptés aux projets actuels et futurs de BrainBox AI.

  • Déploiement de modèles : Collaborer étroitement avec les ingénieurs et chercheurs en AA afin de combler l’écart entre un modèle entraîné et un service déployé, en résolvant les frictions d’ingénierie liées à la mise en production.

  • Surveillance et détection de dérive : Concevoir et déployer le « système immunitaire » de nos modèles — des systèmes qui suivent la dégradation des performances et détectent de façon proactive la dérive des données et des concepts.

  • Soutien technique et programmation : Agir comme ressource de haut niveau en programmation d’IA et fournir un soutien technique à l’équipe élargie afin d’assurer l’atteinte précise des jalons de projet.

  • Initiatives interfonctionnelles : Faire évoluer les méthodologies existantes et développer de nouvelles techniques pour des initiatives couvrant différentes fonctions d’ingénierie et de recherche.

  • Intelligence des données : Repérer et interpréter des tendances complexes dans les ensembles de données afin d’optimiser la performance des algorithmes selon les besoins d’affaires réels.

  • Excellence du code : Rédiger un code exceptionnellement propre, testable et facile à déboguer. Promouvoir la documentation et les meilleures pratiques de développement logiciel tout au long du cycle de vie de l’AA.

Ce dont vous aurez besoin pour réussir :

  • Baccalauréat ou maîtrise en génie logiciel, en informatique ou équivalent.

  • De 5 à 8+ ans d’expérience en génie logiciel, avec une forte spécialisation dans le déploiement et la maintenance de systèmes d’apprentissage automatique.

  • Maîtrise avancée de Python et de la programmation orientée objet (POO), essentielle.

  • Solide expérience des environnements AWS (Lambda, SageMaker, tables Glue, SQS/SNS, API Gateway, CloudWatch) et gestion des rôles IAM pour des déploiements sécurisés.

  • Expertise avec pytest et les cadres de tests unitaires afin d’assurer la fiabilité du code.

  • Connaissance approfondie des environnements Linux et forte propension à automatiser les tâches et flux de travail répétitifs.

  • Bonne compréhension des modèles de déploiement en AA et des architectures LLM actuelles.

  • Capacité à expliquer efficacement des concepts d’ingénierie complexes à des collègues aux profils techniques variés.

  • Membre d’équipe proactif agissant comme personne-ressource, partageant ses connaissances et résolvant efficacement les problèmes liés aux modèles.

Exigences linguistiques

Le bilinguisme français-anglais est requis.

En plus de la maîtrise du français, les personnes retenues doivent posséder une compétence professionnelle complète en anglais afin de soutenir et de collaborer avec des clients, collègues et/ou divers intervenants anglophones.

***English Follows

What you will do:

  • Framework Implementation: Actively participate in the design and hands-on implementation of scalable AI frameworks tailored for BrainBox AI’s ongoing and future projects.

  • Model Deployment: Partner closely with ML engineers and researchers to bridge the gap between a trained model and a deployed service, solving the engineering "friction" that arises during productionization.

  • Monitoring & Drift Detection: Build and deploy the "immune system" for our models—systems that track performance decay and proactively detect data and concept drift.

  • Technical Support & Programming: Serve as a high-level resource in AI programming, providing technical support to the broader team to ensure project milestones are met with precision.

  • Cross-Functional Initiatives: Help evolve existing methodologies and develop new techniques for initiatives that span across different engineering and research functions.

  • Data Intelligence: Identify and interpret complex patterns within datasets to refine and enhance algorithm performance based on real-world business requirements.

  • Code Excellence: Write exceptionally clean, testable, and debuggable code. You will be a champion for documentation and software development best practices within the ML lifecycle.

What you will need to be successful:

  • Bachelor’s or Master’s in Software Engineering, Computer Science, or equivalent.

  • 5-8+ years of experience in software engineering, with a significant focus on the deployment and maintenance of Machine Learning systems

  • Advanced proficiency in Python and Object-Oriented Programming (OOP) is non-negotiable.

  • Strong experience in AWS environments (Lambdas, SageMaker, Glue Tables, SQS/SNS, API Gateway, CloudWatch) and navigating IAM roles for secure deployments.

  • Expert-level familiarity with pytest and unit testing frameworks to ensure code reliability.

  • Deep knowledge of Linux environments and a natural instinct to automate repetitive tasks and workflows.

  • A strong understanding of ML model deployment patterns and current LLM architectures.

  • Ability to effectively communicate complex engineering concepts to colleagues with diverse technical backgrounds.

  • A proactive teammate who acts as a "resource person" for others, sharing knowledge and troubleshooting model issues effectively.

Language Requirements
French-English bilingualism is required.
In addition to fluency in French, successful candidates must have full professional proficiency in English in order to support and collaborate with English-speaking clients, colleagues and/or various stakeholders.

Annual Base Salary Range or Hourly Base Pay Range:

$111,308.33 - $155,435.00

Compensation Type:

Salary

Incentive Eligible:

Yes

Sales Commission Eligible:

No

Disclaimer: We strive to provide competitive compensation for this position, tailored to a variety of factors. The actual compensation will depend on elements such as seniority, merit, geographic location, education, experience,  travel requirements, and union designation.   Our compensation range is generally based on the national average for the country.  Additionally, benefits may vary depending on the region, business alignment, union involvement, and employee status.

We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.


What Trane Technologies employees say

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Workplace

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About Trane Technologies

Sourced by ZipRecruiter

At Trane Technologies and through our businesses including Trane® and Thermo King®, we create innovative climate solutions for buildings, homes, and transportation that challenge what's possible for a sustainable world. We're a team that dares to look at the world's challenges and see impactful possibilities. We believe in a better future when we uplift others and enable our people to thrive at work and at home. We boldly go.

Industry

Industrial machinery manufacturing and machinery manufacturing

Company size

10,000+ Employees

Headquarters location

Davidson, NC, US