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Machine Learning Engineer Jobs in Quebec (NOW HIRING)

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 ...

LawZero

Machine Learning Manager

Montreal, QC

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 ...

Kinaxis Inc.

$100 - $130/hr

In 1984, we started out as a team of three engineers. Today, we have grown to become a global ... What you will do Bring deep expertise in machine learning and applied AI, and you are energized by ...

Targeted Talent

Senior Deep Learning Engineer

Montreal, QC · On-site +1

$130K - $180K/yr

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus will be optimizing neural networks to efficiently run on our hardware and building a model ...

Targeted Talent

Senior Deep Learning Engineer

Quebec, QC · On-site +1

$130K - $180K/yr

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus will be optimizing neural networks to efficiently run on our hardware and building a model ...

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Machine Learning Engineer information

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

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.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

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 engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

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.

More about 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 Machine Learning Engineer jobs in Quebec? For Machine Learning Engineer jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Quebec look for? The top searched job categories for Machine Learning Engineer jobs in Quebec are:
What are popular job titles related to Machine Learning Engineer jobs in QC? For Machine Learning Engineer jobs in QC, the most frequently searched job titles are:
Machine Learning Engineer jobs near you
Infographic showing various 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.
Machine Learning Engineer - (Computer Vision)

Machine Learning Engineer - (Computer Vision)

Jumio

Montreal, QC • On-site

Other

Posted 2 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.

Apply