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Remote Train Ai Jobs in Quebec (NOW HIRING)

Cross Border Talents

Generalist Expert

Montreal, QC · Remote

CA$50 - CA$70/hr

Remote Region: United States / Canada Schedule: Flexible About the Role We're looking for US- or Canada-based Generalist Experts to help train and evaluate frontier AI models. In this role, you'll ...

Danaher

Country Manager

Montreal, QC · On-site +1

CA$150K - CA$175K/yr

Our advanced microscopes and AI-based image analysis solutions enable users to gain profound ... Recruit, train, and develop sales teams using Value Selling. * Maintains rigor in all Standard Work ...

All JobsTrain Ai JobsRemote Train Ai JobsRemote Train Ai Jobs in Quebec

Remote Train Ai information

What is a remote train AI?

A Remote Train AI job typically involves working from home or another remote location to help develop and improve artificial intelligence systems. This can include tasks such as labeling data, providing feedback on AI-generated outputs, or training machine learning models by reviewing and correcting the AI's responses. People in these roles play a crucial part in making AI models more accurate, reliable, and useful for various applications. The job may not require advanced technical skills, making it accessible to a wide range of candidates. Remote Train AI roles are often offered by tech companies or AI research organizations.

What are the key skills and qualifications needed to thrive as a remote train AI?

To thrive as a Remote AI Trainer, you need a strong understanding of machine learning concepts, data annotation processes, and subject-matter expertise relevant to the AI system being trained, often supported by a degree in computer science or a related field. Familiarity with data labeling tools, annotation platforms, and collaboration software is typically required. Attention to detail, critical thinking, and effective communication are essential soft skills for ensuring high-quality data and clear feedback loops. These skills are vital to produce reliable, unbiased training data and facilitate the development of accurate AI models.

What are some common challenges faced when working remotely as a remote train AI, and how can they be addressed?

Working remotely as an AI Trainer often involves managing communication across different time zones and collaborating with team members from diverse backgrounds. Staying aligned on annotation guidelines and project objectives can be challenging without face-to-face interactions. To address these, it's important to leverage collaboration tools, participate in regular team meetings, and proactively seek clarification when needed. Maintaining organized documentation and establishing clear communication channels can also help ensure consistency and high-quality training data.

What is the difference between Remote Train Ai vs Data Annotator?

AspectRemote Train AiData Annotator
Required CredentialsBasic technical skills, training in AI data labelingNone or minimal; often on-the-job training
Work EnvironmentRemote, flexible hours, often part-time or freelancePrimarily remote, may vary by employer
Industry UsageAI development, machine learning projectsAI, computer vision, NLP projects
Common Search/ComparisonRemote Train AiData Annotator

Remote Train Ai and Data Annotator roles both involve labeling data for AI training, often remotely. Remote Train Ai may require some technical understanding and training, while Data Annotators typically need minimal credentials. Both roles are essential in AI development and are commonly found in similar work environments, making them frequently compared by job seekers.

What are popular job titles related to Remote Train Ai jobs in Quebec?

For Remote Train Ai jobs in Quebec, the most frequently searched job titles are:

What job categories do people searching Remote Train Ai jobs in Quebec look for?

The top searched job categories for Remote Train Ai jobs in Quebec are:

Remote Train Ai jobs near you
Infographic showing various Remote Train Ai job openings in Quebec as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution.

Founding Computer Vision Engineer

Assembler AI

Montreal, QC • On-site, Remote

Full-time

Posted 3 days ago

New

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