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New Grad Machine Learning Jobs in Quebec (NOW HIRING)

Desjardins Group

Data internship, Fall 2026

Montreal, QC · Hybrid

Help monitor new advances in AI and support their adoption by Desjardins Group 🤖Machine learning ... Help implement continuous integration and deployment (CI/CD) pipelines for machine learning models

Giesecke+Devrient

Senior Data Scientist

Montreal, QC

We are seeking a highly analytical and execution-focused Senior Data Scientist to join G+D's new AI Hub. The ideal candidate will combine strong expertise in machine learning, statistical modeling ...

Cogeco

Data Scientist

Montreal, QC · On-site

Utilize machine learning techniques to improve customer segmentation, churn prediction, and ... Engineer features by using your business acumen to find new ways to combine disparate internal and ...

Valence Labs

ML Intern, Research

Montreal, QC

Proven track record in machine learning, including designing new architectures, hands-on experimentation, analysis, visualization, and model deployment. * Demonstrated capability to understand and ...

Bentley Systems

Experience pratique en developpement et application de solutions de machine learning * Capacite a ... Capability to research a new topic and to learn quickly * Experience with major cloud providers ...

Intact Financial

Senior Data Scientist

Montreal, QC · Hybrid

Use machine learning and advanced statistical methods to identify trends and patterns in complex ... new member of our growing team. We are an equal opportunity employer At Intact, our Value of ...

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New Grad Machine Learning information

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

To thrive as a New Grad Machine Learning Engineer, you need a solid foundation in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, version control systems like Git, and coursework or certification in data science are highly beneficial. Strong problem-solving abilities, curiosity, and effective communication skills help you collaborate and convey complex technical concepts to diverse teams. These skills and qualities are essential for developing innovative models, ensuring project success, and integrating seamlessly into fast-paced tech environments.

What are some typical challenges new graduates might face when starting out in a machine learning role, and how can they overcome them?

New grad machine learning engineers often encounter challenges such as bridging the gap between academic knowledge and practical, production-level projects. Adapting to real-world data issues, collaborating with cross-functional teams, and understanding scalable deployment can be daunting at first. To overcome these, it's helpful to seek mentorship, proactively ask questions, and dedicate time to learning best practices in code versioning, model evaluation, and team communication. Engaging in code reviews and participating in team discussions can also accelerate the learning curve and foster professional growth.

What are 'New Grad Machine Learning' roles?

New Grad Machine Learning roles are entry-level positions designed for recent graduates who have studied machine learning, artificial intelligence, data science, or related fields. These positions typically involve working with experienced data scientists and engineers to develop, implement, and improve machine learning models and algorithms. New grads in these roles often contribute to projects involving data preprocessing, model training, evaluation, and deployment. The goal is to help new graduates gain hands-on experience and grow their skills in a real-world setting while contributing to the organization's AI initiatives.

What is the difference between New Grad Machine Learning vs Data Scientist?

AspectNew Grad Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some internshipsBachelor's or Master's in CS, Statistics, or related; some experience
Work EnvironmentEntry-level, team-focused, research and developmentData analysis, modeling, cross-functional collaboration
Employer & Industry UsageTech companies, startups, research labsTech, finance, healthcare, consulting firms

New Grad Machine Learning roles typically focus on foundational skills, internships, and entry-level tasks, while Data Scientist positions often require more experience in data analysis and statistical modeling. Both roles are common in tech industries, but Data Scientists usually handle broader data analysis responsibilities.

What are popular job titles related to New Grad Machine Learning jobs in Quebec? For New Grad Machine Learning jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching New Grad Machine Learning jobs in Quebec look for? The top searched job categories for New Grad Machine Learning jobs in Quebec are:
What cities in Quebec are hiring for New Grad Machine Learning jobs? Cities in Quebec with the most New Grad Machine Learning job openings:
New Grad Machine Learning jobs near you
Infographic showing various New Grad Machine Learning job openings in Quebec as of May 2026, with employment types broken down into 4% As Needed, 75% Full Time, 13% Part Time, 4% Temporary, and 4% Contract. Highlights an 49% Physical, 3% Hybrid, and 48% Remote job distribution.

ML Developer Intern (Applied Research)

LawZero

Montreal, QC

Other

Posted 12 days ago

Job description

We are seeking interns in applied machine learning (ML) to join our team working on using the Scientist AI in real-world applications. In this role, you will work closely with ML research scientists, product stakeholders and domain experts to start applying the Scientist AI to domain-specific problems.

Key responsibilities

  • Accelerate applied research, model training and inference, and iterate on implementing models for real-world applications (that will form the basis of LawZero's future solutions offerings).
  • Design and implement workflows for research experiments across simulated environments and real-world applications.
  • Develop datasets, tooling, dashboards, and libraries to adapt, monitor, interpret, and evaluate models in the context of real-world applications.
  • Establish, document, and maintain best practices for ML model development workflows.
  • Redesign and adapt research ideas and prototypes into robust production-grade tools and solutions.
  • Deeply understand customer use-cases to inform training strategies and surface edge cases.

Skills and qualifications:

  • A degree in a relevant computer science field (e.g., computer science, computer engineering, software engineering) is required, along with an advanced degree (MSc or higher) in machine learning or equivalent work experience.
  • Ability to collaborate effectively with cross-functional teams, document best practices, and stay updated with the latest advancements in ML and software development.
  • Familiarity with cloud platforms (e.g., AWS, GCP, Azure)
  • Familiarity with containerization tools (e.g., gRPC, Docker, Kubernetes).
  • Familiarity with data infrastructures and platforms (e.g., vector databases).

Nice to have:

  • Familiarity with workload managers (e.g., Ray, SLURM)
  • Industry experience in designing and implementing complex machine learning workflows on high performance computing devices using PyTorch, TensorFlow, or JAX.
  • Experience building new ML products from scratch, or based on research prototypes.
  • Experience working alongside researchers.

Apply