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Junior Machine Learning Engineer Jobs in Montreal, QC

Jumio

Senior Machine Learning Engineer

Montreal, QC

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 ...

Unitytech

Senior Machine Learning Engineer

Montreal, QC

CA$147K - CA$220K/yr

Integrate Machine Learning models seamlessly within real-time game engine constraints, optimizing ... Mentor engineers and researchers while cultivating a technical culture of innovation, excellence ...

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 ...

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 JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Montreal, QC

Junior Machine Learning Engineer information

See Montreal, QC salary details

$25.9K

$118.8K

$206.9K

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

As of Aug 25, 2026, the average yearly pay for junior machine learning engineer in Montreal, QC is $118,823.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,245.00 and $148,580.00 per year, depending on experience, location, and employer.

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.

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 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 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.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn 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.
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For Junior Machine Learning Engineer jobs in Montreal, QC, the most frequently searched job titles are:

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Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Montreal, QC as of August 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 73% In-person, 8% Hybrid, and 19% Remote job distribution, with an average salary of $118,823 per year, or $57.1 per hour.

Machine Learning Engineer - (Computer Vision)

Jumio

Montreal, QC

Full-time

Re-posted 5 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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