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Junior Machine Learning Engineer Jobs in Quebec (NOW HIRING)

LawZero

Senior Distributed ML Engineer

Montreal, QC

We are seeking a senior distributed 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 ...

AC780

Paytm Senior Software Engineer

Montreal, QC

Work with our machine learning engineers to put cutting edge deep learning algorithms in production. * Develop tools and contribute to open source wherever possible. * Adopt problem solving as a way ...

Autodesk

Senior Applied AI/ML Developer

Montreal, QC · Remote

... 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 ...

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LawZero

Senior ML Research Developer

Montreal, QC

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 ...

Maya HTT

AI Presales Engineer

Montreal, QC

Our team delivers extensive engineering and CAE simulation expertise along with cutting-edge digitalization solutions, such as AI, machine learning, IIoT, operational technologies, and Industry 4.0.

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Showing results 1-20

All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Quebec

Junior Machine Learning Engineer information

See Quebec salary details

$26K

$119.2K

$207.5K

How much do junior machine learning engineer jobs pay per year?

As of Jul 23, 2026, the average yearly pay for junior machine learning engineer in Quebec is $119,158.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,500.00 and $149,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Junior Machine Learning Engineer, and why are they important?

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

What kinds of projects and responsibilities can a Junior Machine Learning Engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

What does a junior machine learning engineer do?

A junior machine learning engineer assists in developing, testing, and deploying machine learning models under supervision. They work with data preprocessing, feature engineering, and use tools like Python and libraries such as TensorFlow or scikit-learn to support AI projects. This role often requires foundational knowledge of algorithms, programming, and data analysis.

How much does a junior machine learning engineer make?

A junior machine learning engineer typically earns between $70,000 and $100,000 annually, depending on location, education, and industry. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level often requires advanced degrees, specialized certifications, and a strong track record of impactful projects.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data science, and programming with tools like Python and TensorFlow. These positions usually involve leadership, strategic planning, and significant experience, and they tend to be found in large tech companies or specialized AI firms.

What Does a Junior Machine Learning Engineer Do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

More about Junior Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Quebec? The most popular types of Machine Learning Engineer jobs in Quebec are:
What are popular job titles related to Junior Machine Learning Engineer jobs in Quebec? For Junior Machine Learning Engineer jobs in Quebec, the most frequently searched job titles are:
What cities in Quebec are hiring for Junior Machine Learning Engineer jobs? Cities in Quebec with the most Junior Machine Learning Engineer job openings:
Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Quebec as of July 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $119,158 per year, or $57.3 per hour.

Junior Data Scientist

Expretio Technologies

Montreal, QC • On-site

Full-time

Posted 5 days ago

Job description

Join our team and take your career to the next level.

Job Summary:

Expretio recherche une Data Scientist junior pour rejoindre son equipe R&D dynamique basee a Montreal. Au sein d'une equipe AI, vous contribuerez au developpement et au support d'Appia, notre solution phare de Revenue Management pour l'industrie ferroviaire, en soutenant l'analyse de donnees, la modelisation predictive et l'analyse du comportement d'achat des clients.
En tant que Data Scientist junior, vous evoluerez dans un role a forte composante operationnelle et analytique : preparation et validation des donnees, investigations sur la qualite des donnees, tableaux de bord et production de resultats clairs pour l'equipe. En parallele, vous serez amene a developper des preuves de concept en Python visant a contribuer a l'amelioration de composants de la chaine d'analyse et de monitoring.
Ce poste constitue un point d'entree ideal dans l'ecosysteme d'Intelligence Artificielle et de Revenue Management d'Expretio. Vous collaborerez etroitement avec des Data Scientists, des developpeurseuses IA et des scientifiques en recherche operationnelle experimentes, tout en developpant vos competences techniques et votre connaissance du domaine. Il s'agit d'un role pense pour evoluer : a mesure que vous progresserez, la science des donnees et la modelisation prendront une plus grande part de vos responsabilites.
------------------------------------ ENGLISH -----------------------
Expretio is looking for a Junior Data Scientist to join its dynamic R&D team based in Montreal. As part of an AI team, you will contribute to the development and support of Appia, our flagship Revenue Management solution for the rail industry, supporting data analysis, predictive modeling, and customer purchasing behavior analysis.
As a Junior Data Scientist, you will work in a role with a strong operational and analytical focus: data preparation and validation, data quality investigations, dashboards, and the production of clear results for the team. In parallel, you will develop proof-of-concept solutions in Python aimed at contributing to the improvement of components within our analysis and monitoring chain.
This role is an ideal entry point into Expretio's Artificial Intelligence and Revenue Management ecosystem. You will work closely with experienced Data Scientists, AI developers, and operations research scientists, while developing your technical skills and domain knowledge. This role is designed to grow: as you progress, data science and modeling will take on a greater share of your responsibilities.

Job Description:

Votre role

Analyse de donnees et soutien operationnel

Soutenir les investigations sur la qualite des donnees et les analyses statistiques

Contribuer a la conception et la mise a jour de tableaux de bord et a la visualisation de donnees

Produire des syntheses claires et des rapports de resultats analytiques

Utiliser les outils d'IA generative pour appuyer l'analyse, l'extraction et les taches d'outillage interne

Participer a l'evolution et l'automatisation de la chaine de calibration et de monitoring

Verifier et documenter la qualite et la coherence des resultats produits

Science des donnees et modelisation

Developper en Python des preuves de concept pour des composants d'analyse et de modelisation predictive

Contribuer a l'amelioration des composants et algorithmes mettant en jeu de la science des donnees

Creer des tests de validation de modeles et la documentation des fonctionnalites analytiques

Participer a l'amelioration de l'architecture de science des donnees et des modeles predictifs dans Appia

Collaboration et developpement

Collaborer avec les clients pour comprendre les comportements et adapter les modeles a leurs besoins

Collaborer avec l'equipe de Support pour analyser et resoudre les problemes de Production

Participer aux retrospectives et proposer des ameliorations continues

Profil recherche

Formation et experience

Baccalaureat en science des donnees, mathematiques, statistiques ou domaine connexe (maitrise un atout)

Experience de stage ou de projet academique en apprentissage automatique et en analyse de donnees

Toute formation complementaire pertinente en science des donnees est consideree comme un atout

La maitrise de l'anglais, tant a l'ecrit qu'a l'oral, est requise pour permettre une communication efficace dans un environnement organisationnel et commercial international. L'entreprise opere a l'echelle mondiale, avec des clients a travers le monde, faisant de l'anglais la langue de travail commune entre les regions.

Competences techniques

Competences en analyse exploratoire de donnees et en controle de la qualite des donnees

Capacite a interpreter les donnees et a en tirer des observations exploitables

Bonne connaissance de Python pour la manipulation et l'analyse de donnees (pandas)

Comprehension des modeles de Machine Learning, notamment les methodes ensemblistes (Random Forest, bagging) et les algorithmes de boosting (XGBoost, LightGBM, gradient boosting)

Notions de modelisation predictive et d'analyse de series temporelles

Bonnes bases en SQL et en manipulation de donnees

Maitrise d'outils de visualisation de donnees (Power BI equivalent), incluant la conception de rapports interactifs

Capacite a communiquer clairement des resultats analytiques en interne comme aux clients

Utilisation des outils d'IA generative pour des taches d'analyse

Capacite a communiquer efficacement en francais et en anglais

Atout : Experience avec Google OR-Tools dans un contexte d'optimisation

Qualites personnelles

Curiosite et forte volonte d'apprendre de nouvelles technologies et concepts

Souci du detail dans l'analyse de donnees et la validation de modeles

Capacite a communiquer avec des parties prenantes variees, en adaptant son discours a chaque audience

Aptitude au travail d'equipe et a recevoir la retroaction de maniere constructive

Sens de la rigueur et de la gestion du temps

Conscience des enjeux d'IA responsable lors de l'utilisation d'outils d'IA generative

Environnement technique

Methodologies : Agile (Scrum), Lean (Kanban)

Science des donnees : Python, pandas, PyTorch, LightGBM, MLflow

Bases de donnees : SQL (PostgreSQL), NoSQL (MongoDB)

Infrastructure : Docker/Podman, GitLab, Jenkins

Outils : Claude, Pycharm, Office 365, Jira, Confluence, PowerBI, Miro
---------------- ENGLISH -----------------------

Key Responsibilities

Data Analysis and Operational Support

Support data quality investigations and statistical analyses

Contribute to the design and updating of dashboards and data visualizations

Produce clear summaries and reports of analytical results

Use generative AI tools to support analysis, extraction, and internal tooling tasks

Participate in the evolution and automation of the calibration and monitoring chain

Verify and document the quality and consistency of the results produced

Data Science and Modeling

Develop proof-of-concept solutions in Python for analysis and predictive modeling components

Contribute to the improvement of components and algorithms involving data science

Create model validation tests and documentation for analytical features

Contribute to the improvement of the data science architecture and predictive models within Appia

Collaboration and Development

Work with clients to understand behaviors and adapt models to their needs

Work with the Support team to analyze and resolve Production issues

Take part in retrospectives and propose continuous improvements

What you bring to the role

Preferred Education and Experience

Bachelor's degree in data science, mathematics, statistics, or a related field (Master's an asset)

Internship or academic project experience in machine learning and data analysis

Any relevant additional training in data science is considered an asset

Technical Skills

Skills in exploratory data analysis and data quality control

Ability to interpret data and derive actionable insights

Solid knowledge of Python for data manipulation and analysis (pandas)

Understanding of Machine Learning models, particularly ensemble methods (Random Forest, bagging) and boosting algorithms (XGBoost, LightGBM, gradient boosting)

Familiarity with predictive modeling and time series analysis

Solid foundations in SQL and data manipulation

Proficiency with data visualization tools (Power BI or equivalent), including the design of interactive reports

Ability to clearly communicate analytical results, both internally and to clients

Use of generative AI tools for analysis tasks

Ability to communicate effectively in French and English

Asset: Experience with Google OR-Tools in an optimization context

Other Key Skills and Competencies

Curiosity and a strong willingness to learn new technologies and concepts

Attention to detail in data analysis and model validation

Ability to communicate with a variety of stakeholders, tailoring message to each audience

Ability to work in a team and receive feedback constructively

Rigor and good time management

Awareness of responsible AI considerations when using generative AI tools

Technical Environment

Methodologies: Agile (Scrum), Lean (Kanban)

Data Science: Python, pandas, PyTorch, LightGBM, MLflow

Databases: SQL (PostgreSQL), NoSQL (MongoDB)

Infrastructure: Docker/Podman, GitLab, Jenkins

Tools: Claude, Pycharm, Office 365, Jira, Confluence, PowerBI, Miro

Worker Type:

Regular

Number of Openings:

1

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