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Machine Learning Engineer Intern Jobs in Quebec (NOW HIRING)

ALS

Junior Machine Learning Engineer

Montreal, QC · On-site

CA$80K - CA$95K/yr

As a Junior Machine Learning Engineer on the Global AI Team, you will support the design, development, deployment, and maintenance of AI and machine learning solutions. You will work with data ...

Jumio

Senior Machine Learning Engineer

Montreal, QC · On-site

We're looking for a Senior Machine Learning Engineer with strong computer vision expertise to join our Biometrics team. You'll ramp up on the biometrics domain while contributing to the design ...

Scientific Games

$120 - $180/hr

Position SummaryAbout the RoleWe are looking for a Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization. This team will create the ...

SAP

Senior AI Machine Learning Engineer

Montreal, QC · On-site

About the Role We are hiring a Senior Machine Learning Engineer Scientist to lead the development of scalable graph-based and transformer-based modeling systems, along with production-grade ML ...

LawZero

Machine Learning Manager

Montreal, QC · On-site

We are seeking a Machine Learning (ML) Manager to join our growing team dedicated to a novel AI ... You will bridge the gap between cutting-edge scientific theory and large-scale engineering ...

Coveo

Backend Development Intern, Fall 2026

Quebec, QC · Hybrid

... machine learning? Are you curious about the systems that power information retrieval, index ... As an intern, you'll gain practical experience with regular engineering workflows while also owning ...

Td

Applied Machine Learning Scientist II

Montreal, QC · On-site

CA$125K - CA$154K/yr

We're looking for a highly motivated Applied Machine Learning Scientist II to join our AI2 team. In ... Experience with structured and unstructured data, feature engineering, and model interpretability ...

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All JobsMachine Learning Engineer Intern JobsMachine Learning Engineer Intern Jobs in Quebec

Machine Learning Engineer Intern information

See Quebec salary details

$23K

$120.7K

$215.5K

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

As of Aug 13, 2026, the average yearly pay for machine learning engineer intern in Quebec is $120,739.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,500.00 and $164,000.00 per year, depending on experience, location, and employer.

What do machine learning engineer interns do?

Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.

What is a machine learning engineer intern?

A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.

What skills and qualifications are needed to thrive as a machine learning engineer intern?

To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.

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 Machine Learning Engineer Intern jobs in Quebec? For Machine Learning Engineer Intern jobs in Quebec, the most frequently searched job titles are:
What cities in Quebec are hiring for Machine Learning Engineer Intern jobs? Cities in Quebec with the most Machine Learning Engineer Intern job openings:
Machine Learning Engineer Intern jobs near you
Infographic showing various Machine Learning Engineer Intern job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $120,739 per year, or $58 per hour.

Machine Learning Engineer - (Computer Vision)

Jumio

Montreal, QC • On-site

Full-time

Posted 23 days ago

Job description

Machine Learning Engineer - (Computer Vision)

We're looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You'll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process.

What You'll Do
  • Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition)
  • Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
  • Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX
  • Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps.
  • Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS.
  • Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.
What We're Looking For
  • Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
  • Deep expertise in computer vision and biometrics, especially face recognition.
  • Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
  • Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code.
  • Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.
  • Cloud Native: Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).
Nice to Have
  • Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
  • Large Scale Search: Experience with vector databases (e.g., Milvus, Faiss) and approximate nearest neighbor (ANN) search algorithms.
  • Familiarity with privacy, security, and compliance in biometric systems.
  • Mobile/Edge Experience: Experience porting models to edge or mobile devices utilizing frameworks such as CoreML, LiteRT, and/or TFLite.
  • Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training.
  • Strong communication skills.

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