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Data Annotation For Ai Jobs in Quebec (NOW HIRING)

... data-to-decision stories. * Ensure all customer-facing materials comply with AI governance, compliance, and security standards. * Act as the primary contact for NDI, articulating its value and ...

You build, deploy, and iterate the AI solution end-to-end: data pipelines, edge model deployment ... Travel: Up to 25% for on-site commissioning, deep discovery, troubleshooting, and customer​ ...

Position Summary We are seeking a Senior Data Scientist with deep expertise across the full ... Serve as the technical owner for AI model performance and production incident resolution.

Perform exploratory data analysis (EDA), model diagnostics, and data quality assessments ... Serve as the technical owner for AI model performance and production incident resolution.

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Data Annotation For Ai information

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

How much do AI data annotators make?

AI data annotators typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Some positions may offer freelance or project-based pay, with rates varying accordingly.

Is data annotation AI job real?

Yes, data annotation for AI is a real job that involves labeling data such as images, text, or videos to help train machine learning models. It often requires attention to detail and familiarity with annotation tools, and roles can be found in tech companies and AI development environments.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

Can you use data annotation for AI?

Data annotation for AI involves labeling and categorizing data such as images, text, or audio to train machine learning models. Data annotation jobs require attention to detail and often involve using specialized tools or platforms; they are essential for developing accurate AI systems.

What does an AI data annotator do?

An AI data annotator labels and tags data such as images, videos, text, or audio to help train machine learning models. They use specialized tools to ensure data is accurately annotated according to project guidelines, which is essential for developing effective AI systems.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.
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Infographic showing various Data Annotation For Ai job openings in Quebec as of June 2026, with employment types broken down into 1% Locum Tenens, 42% Full Time, 11% Part Time, 3% Temporary, 41% Contract, and 2% Nights. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution.

Mathematical Scientist for AI Safety Research

LawZero

Montreal, QC

Other

Medical, Retirement, PTO

Posted 5 days ago


Job description

Frontier AI companies are throwing billions of dollars into scaling existing architectures and methods such as next-token-prediction, direct preference optimization (DPO), reinforcement learning with human feedback (RLHF), and reinforcement learning with verified rewards (RLVR). These methods are very powerful, yet fundamentally flawed, resulting in misalignment, sycophancy, systematic biases, and other forms of harmful behavior that are already having severely negative consequences in our society.

LawZero is a non-profit founded by Yoshua Bengio developing a fundamentally new approach, the Scientist AI. We are inspired by the fact that scientific theories are both generally useful and, unlike an untrusted agent, equivariant to the consequences of their use (for a broad overview, see our blogpost). We aim not only to build a novel, safe-by-design system, but to construct a theoretical blueprint for safe AI systems, which are at once capable and come with probabilistic safety guarantees, under finite data and compute constraints.

We are seeking an outstanding theoretical researcher to contribute to the foundational and theoretical efforts here working with Yoshua Bengio and our world-class team of researchers and engineers.

Key responsibilities

  • Boldly go where no one has gone before in exploring new AI system architectures, theoretical frameworks, and new safety mechanisms in search of a  blueprint for safe AGI and corresponding safety guarantees..
  • Work closely with Yoshua Bengio and other leading researchers to analyze and refine the formal requirements needed to safely build and deploy the Scientist AI, including proving probabilistic safety guarantees under finite data and compute constraints; and, as the system matures, helping our research teams with empirical validation.
  • Analyze, evaluate and contribute to the existing theoretical work already undertaken at LawZero by Yoshua Bengio and leading researchers, including the writing and dissemination of scientific papers.
  • Read vigorously to stay up-to-date with relevant AI safety and machine learning research and deeply understand the risks posed by frontier models, including misalignment, reward hacking, instrumental goals, etc.
  • Scrutinize and contribute to LawZero's broader research agenda in order to guide and prioritize experiments to answer critical safety questions efficiently, in a way that does not prevent the highest level of capability.
  • Share your knowledge about AI Safety and help researchers and engineers at LawZero better design and implement new ML algorithms and experiments.

Skills and qualifications

  • A PhD  in machine learning, computer science, mathematics, statistics, or an adjacent area relevant to the Scientist AI research program.
  • A track record of research, especially in the formalization and fruitful technical analysis of subtle natural-language intuition.
  • Strong interest and ideally experience with technical AI safety, including an exceptional ability for abstract systems thinking.
  • A deep understanding of the mathematical underpinnings commonly used in the design and analysis of probabilistic machine learning, especially for neural networks and language models. 
  • The perseverance to work through hard and ill-defined problems, and to use confusion and dead-ends as challenges to dig deeper and further understanding.
  • Clear communications: the ability to articulate complex ideas verbally and in writing to both technical and non-technical audiences.
  • The ability to work in a collaborative environment and contribute to collective research goals.

Nice to have:

  • The ability to regularly travel alongside Yoshua and team members to conferences is a big plus, as it will significantly accelerate the work!

What we offer

  • The chance to contribute meaningfully to a globally critical initiative
  • Comprehensive health benefits (including mental health and wellness management account)
  • 20 days of vacation per year upon start
  • Employer contribution of 4% to your retirement savings, with no required employee match
  • Additional compensation totaling 8% of your salary to apply towards additional retirement savings or bonuses (independent of group and individual performance)
  • A team of passionate world-class experts in their field
  • A collaborative and inclusive work environment in our vibrant office space in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation