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Entry Level Computer Vision Deep Learning Engineer Jobs in L Assomption, QC

Maya HTT

AI Team Lead

Montreal, QC · Remote

Lead and mentor a team of AI/ML developers/engineers, computer vision specialists, and frontend ... Strong background in deep learning (e.g., CNNs, transformers, detection/segmentation models)

SIA

Generative AI Consultant

Montreal, QC

... areas like computer vision, MLOps, and deep learning, partnering closely with our business ... Advanced Prompt Engineering: Design and optimize effective prompts (e.g., few-shot, Chain/Tree ...

Stay22

Senior ML/DL Developer

Montreal, QC · On-site

Machine learning et deep learning Excellente maîtrise de Python et des bibliothèques de deep ... M.S. in Computer Science, Machine Learning, Data Mining, Statistics, or a related technical field ...

Intact Financial

Data Scientist II

Montreal, QC · On-site

Machine Learning Expertise: Deep understanding of machine learning theories and methodologies ... Expertise in one or more areas such as computer vision, natural language processing, or artificial ...

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Entry Level Computer Vision Deep Learning Engineer information

What does an entry level computer vision deep learning engineer do?

An Entry Level Computer Vision Deep Learning Engineer works on developing and implementing algorithms that allow computers to interpret and understand visual information from the world, such as images or videos. They typically use deep learning techniques, especially neural networks, to build models for tasks like object detection, facial recognition, and image classification. Their responsibilities may include data preprocessing, model training and evaluation, writing code (often in Python), and collaborating with senior engineers on real-world projects. This role is ideal for those who have a strong foundation in machine learning, programming, and mathematics, but are just starting their careers in the field.

What types of projects do entry level computer vision deep learning engineers typically work on, and how is their work structured within a team?

As an entry-level Computer Vision Deep Learning Engineer, you can expect to contribute to projects like object detection, image classification, and model optimization for real-world applications. Your tasks may include data preprocessing, training and evaluating neural networks, and writing code to integrate models into products or pipelines. You'll often collaborate closely with senior engineers, data scientists, and product managers, typically working in agile teams where regular code reviews and knowledge sharing are common. This collaborative environment not only helps you learn best practices but also provides opportunities to gradually take on more responsibility as your skills develop.

What are the key skills and qualifications needed to thrive as an entry level computer vision deep learning engineer, and why are they important?

To thrive as an Entry Level Computer Vision Deep Learning Engineer, you need a solid understanding of computer vision fundamentals, deep learning concepts, and programming skills in languages like Python, along with a relevant degree in computer science, engineering, or a related field. Familiarity with frameworks such as TensorFlow or PyTorch, experience with OpenCV, and knowledge of version control systems like Git are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you collaborate within teams and tackle complex challenges. These skills and qualities are crucial for developing, deploying, and optimizing computer vision solutions that meet real-world business needs.
Entry Level Computer Vision Deep Learning Engineer jobs near you
Infographic showing various Entry Level Computer Vision Deep Learning Engineer job openings in L'Assomption, QC as of June 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution.

Founding Computer Vision Engineer

Montreal, QC • On-site, Remote

Assembler AI
See more jobs at Assembler AIexternal

Full-time

Posted 8 days ago

Key responsibilities

  • Train, evaluate, and improve vision models on real production video

  • Own the training datasets by sourcing, selecting data, defining label schemas, and reviewing annotation quality

  • Investigate model failures on footage, identify root causes, and work with the annotation team to target data for performance improvement

Job description

The Opportunity

Manufacturing powers the global economy at $50T a year and it relies heavily on human dexterity and skill to produce the goods we rely on every day. Yet manufacturers have little visibility when issues arise at manual assembly stations, impacting productivity and quality.

Assembler AI is changing that.

We use computer vision and artificial intelligence to help manufacturers improve quality, reduce waste, increase throughput, and enable frontline employees to perform at their best.

Backed by Diagram Ventures, we're building a category-defining company at the intersection of AI, manufacturing, and operational excellence.

The role

We're hiring a founding computer vision engineer. You'll work directly with the Head of AI on the perception models behind our products, and take ownership of the datasets and evaluation work that make them perform in the field.This is an applied, hands-on role. Our hardest problems aren't about picking the newest architecture , they're about building the right training data, evaluating honestly enough to know what actually improved, and spending real time in the footage to understand what a model is getting wrong. If you like problems where the answer is in the data rather than the paper, you'll do well here.

What you'll do

  • Train, evaluate, and improve vision models on real production video
  • Own our training datasets: sourcing and selecting data, defining label schemas, and reviewing annotation quality
  • Design evaluations that reflect real deployment conditions, and report results clearly and honestly
  • Investigate model failures directly on footage and identify their root causes
  • Work with our annotation team to target the data most likely to improve performance
  • Run experiments end to end: from question, to training runs, to a result someone can act on

Requirements

  • Bachelor's in computer science, engineering, mathematics, or a related field ( or equivalent practical experience). A Master's in computer vision, machine learning, or a related area is welcome but not required
  • 2+ years of hands-on applied computer vision experience, ideally in industry or on systems deployed beyond a research environment
  • You've trained and deployed a vision model on real-world data end to end, including the messy parts
  • Strong Python and PyTorch
  • Solid understanding of evaluation: train/test splits, precision/recall tradeoffs, and why a strong validation score doesn't always mean a good model in production
  • Comfortable spending significant time reviewing video footage; understanding what the camera actually sees is a real part of this job
  • Willing to say "I don't know" and "I think I got that wrong." We'd rather hear it early
  • Professional working proficiency in French and English
  • Based in Canada

Technical environment

Required:

  • Git and a standard branch/review workflow
  • Linux — comfortable working entirely over SSH on remote GPU machines: bash, background jobs, services, reading logs
  • Cloud — hands-on experience with AWS and/or GCP, including compute, storage, and managing your own training environments
  • PyTorch and CUDA — GPU-based training, checkpoint management, and experiment tracking.
  • OpenCV and ffmpeg — decoding, cropping, and processing video at scale
  • NumPy, pandas, scikit-learn, and standard annotation data formats

Nice to have:

  • Experience with video and temporal models, not just single images
  • Object tracking and multi-object association
  • Model export and inference optimization
  • Experience with annotation platforms and running an annotation workflow
  • Exposure to manufacturing, robotics, or industrial inspection

Why join

You'll be the second person on the AI team, with direct ownership of a core part of the product and a very short path from your work to something running on a live production line.

Compensation & Benefits

  • Competitive salary and stock options
  • Competitive Health benefits
  • Health and wellness spending account, from $500 to $1,000 annually
  • Latest MacBook and modern sales tooling
  • Opportunity to grow as the company scales