ZipRecruiter

Log In

1

Machine Vision Jobs in Quebec (NOW HIRING)

Maya HTT

AI Team Lead

Montreal, QC · Hybrid

The AI Team Lead leads a multidisciplinary team delivering production-grade AI solutions for industrial and manufacturing environments, including telemetry analytics, machine vision, inspection ...

Motorola

Director of Computer Vision

Quebec, QC · On-site

Our AI-powered security solutions integrate advanced video analytics, machine learning, and ... Technical Vision, Engineering Leadership, and Execution: Provide executive technical leadership to ...

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 ...

Solution SFT

Machiniste conventionnel + CNC

Laval, QC · On-site

Le rôle de l'opérateur CNC multi-machines ou machiniste CNC consiste à produire des pièces ... Assurances complètes (santé, dentaire, vision) + télémédecine * Régime de retraite avec ...

Solution SFT

Machiniste - Soir

Laval, QC · On-site

... de machine à contrôle numérique. Le machiniste CNC s'assure de la qualité des produits ... Assurance collective (soins médicaux, dentaires, vision et soins paramédicaux, assurance vie ...

Solution SFT

Machiniste - Soir

Laval, QC

... Machine Gantry - Horaire de soir Role et raison d'etre du poste : Le role du machiniste CNC est de ... Assurance collective (soins medicaux, dentaires, vision et soins paramedicaux, assurance vie ...

Atelier Val-d'Or inc.

Régime d'avantages sociaux complet (vie, invalidité, dentaire, vision, etc.) * Horaire flexible ... Optimiser le temps machine en opérant plus d'une machine * Signaler rapidement toute anomalie ...

BRH

Expérience sur boring mill CNC et/ou machine 5 axes, un atout important; * Capacité à effectuer ... Lunettes de sécurité Sécuro-Vision payées par l'employeur; * RVER avec cotisation de ...

next page

Showing results 1-20

All JobsMachine Vision JobsMachine Vision Jobs in Quebec

Machine Vision information

What is the difference between Machine Vision vs Computer Vision?

AspectMachine VisionComputer Vision
Required CredentialsTypically requires engineering degrees, certifications in image processing or automationOften requires computer science or AI-related degrees, certifications in deep learning or AI
Work EnvironmentIndustrial settings, manufacturing plants, quality control labsResearch labs, software development environments, AI startups
Industry UsageManufacturing, automation, roboticsHealthcare, autonomous vehicles, multimedia analysis
Search & Comparison IntentFocuses on industrial applications and hardware integrationFocuses on algorithms, software, and AI models

Machine Vision and Computer Vision are related fields but differ mainly in application and environment. Machine Vision is primarily used in industrial settings for automation and quality control, requiring specialized hardware and engineering skills. Computer Vision is broader, often involving AI and software development for applications like autonomous vehicles and image analysis. Understanding these differences helps in choosing the right career path or job focus within the tech industry.

What are some common challenges faced by professionals working in Machine Vision roles?

Professionals in Machine Vision often encounter challenges related to integrating vision systems with existing automation equipment and ensuring reliable performance in variable lighting or environmental conditions. Troubleshooting hardware-software interactions and maintaining accurate image recognition in high-speed production environments are also frequent hurdles. Collaborating closely with cross-disciplinary teams, such as robotics engineers and production managers, is essential to address these challenges effectively and deliver robust solutions.

What is machine vision?

Machine vision is a field of technology and engineering that enables computers and machines to interpret and process visual information from the world, typically using cameras and image processing software. It is widely used in industrial automation for tasks such as inspection, measurement, and object recognition. Machine vision systems can analyze images to detect defects, guide robots, and ensure quality control. They play a crucial role in manufacturing, logistics, and many other sectors where visual inspection or guidance is required.

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

To thrive as a Machine Vision Engineer, you need strong knowledge of image processing, computer vision algorithms, and programming languages like Python or C++, typically supported by a degree in engineering or computer science. Familiarity with machine vision software (such as OpenCV, HALCON, or MATLAB), industrial cameras, and hardware integration is essential. Attention to detail, problem-solving ability, and effective communication are standout soft skills in this role. These skills and qualities are crucial for developing robust vision systems that ensure accuracy and efficiency in automated inspection and quality control processes.
Machine Vision jobs near you
Infographic showing various Machine Vision job openings in Quebec as of July 2026, with employment types broken down into 84% Full Time, 12% Part Time, 2% Contract, and 2% Nights. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution.
Machine Learning Engineer - (Computer Vision)

Machine Learning Engineer - (Computer Vision)

Jumio

Montreal, QC • On-site

Other

Posted 6 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