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Data Annotation Jobs in Ontario (NOW HIRING)

Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation: $50-$70/hour Location: Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from ...

Network Engineer

Toronto, ON · Remote

CA$50 - CA$70/hr

Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation: $50-$70/hour Location: Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from ...

Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation: $50-$70/hour Location: Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from ...

Network Support Engineer

Toronto, ON · Remote

CA$50 - CA$70/hr

Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation: $50-$70/hour Location: Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from ...

Network System Engineer

Toronto, ON · Remote

CA$50 - CA$70/hr

Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation: $50-$70/hour Location: Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from ...

Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning data pipelines. * Design tests for machine ...

Engineer, AI & Software Testing

Toronto, ON · Hybrid

CA$95K - CA$145K/yr

Proficient in handling training and validation data, segmentation and annotation process * Proficient in programming python * Proficient in machine learning and AI frameworks such as Tensorflow ...

Rather than relying solely on human preference data, we can ground reinforcement learning in the ... Drive human-in-the-loop evaluation with high annotation quality and sound scientific methodology

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Data Annotation information

Is data annotation a legitimate?

Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It is commonly performed remotely and requires attention to detail, basic technical skills, and familiarity with annotation tools. Many companies hire data annotators as part of their AI development teams.

What does a typical workday look like for someone in a Data Annotation role?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

What is a Data Annotation job?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

What are the key skills and qualifications needed to thrive in the Data Annotation position, and why are they important?

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

What does a data annotator do?

A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. Attention to detail and knowledge of annotation tools are important for this role.

Do people actually make money on data annotation?

Data annotation jobs can provide a source of income, with pay rates varying based on the complexity of tasks, platform, and experience. Many annotators earn hourly or per-task wages, but earnings often depend on the volume of work completed and the employer's pay structure.

Is it hard to get hired for data annotation?

Getting hired for a data annotation role generally depends on the employer's requirements, such as attention to detail and basic computer skills. Many positions are entry-level and may not require prior experience or certifications, making them accessible to a wide range of applicants. However, competition can vary based on the number of available jobs and the quality of applicants.
What are popular job titles related to Data Annotation jobs in Ontario? For Data Annotation jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Data Annotation jobs in Ontario look for? The top searched job categories for Data Annotation jobs in Ontario are:
What cities in Ontario are hiring for Data Annotation jobs? Cities in Ontario with the most Data Annotation job openings:
Infographic showing various Data Annotation job openings in Ontario as of June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 33% Hybrid, and 67% Remote job distribution.

Machine Learning Analyst

Fidelity International

Toronto, ON • Hybrid

Full-time

Posted yesterday


Job description

Job Description

Please note:

  • Current work authorization for Canada is required for all openings.
  • You will be working on a flexible hybrid schedule as part of Fidelity's dynamic working arrangement.
  • This is a full-time regular opportunity.
  • The work location for this role is 483 Bay Street in Toronto until approximately late 2026, when the work location will change to the new Mississauga office at 3 Robert Speck Parkway

Who We Are

At Fidelity, we've been helping Canadian investors build better financial futures for over 35 years. We offer individuals and institutions a range of trusted investment portfolios and services - and we're constantly seeking to find new and better ways to help our clients. As a privately owned company, we boldly embrace innovation in all areas as we continue to grow our business into the future.

Working with us means you'll be part of a diverse and dedicated group of people who make a real difference for our clients and communities every day. You'll have a wide range of opportunities to grow and develop your career in an inclusive environment where you'll feel valued and supported to be your best - both personally and professionally.

Fidelity Investments Canada is looking for a highly motivated and creative 'Machine Learning Analyst' to Fidelity Investments Canada is looking for a highly motivated and creative 'Machine Learning Analyst' to develop innovative AI/ML solutions to complex business challenges. Critical to the role's success will be the individual's penchant for continuous learning and a laser focus on delivering practical applications in a quickly evolving technical environment. As an ML Analyst, you will be collaborating with an interdisciplinary team that leverages large datasets using highly scalable computational resources to deliver end-to-end AI/ML based projects. You will be responsible for conducting exploratory data analysis, data pre-processing and transformation, developing ML algorithms, and assisting with deployment using both on-premise and cloud-based platforms.

What You'll Do

As an ML Analyst, you will be collaborating with an interdisciplinary team that leverages large datasets using highly scalable computational resources to deliver end-to-end AI/ML based solutions. You will be responsible for conducting exploratory data analysis, data pre-processing and transformation, developing ML algorithms, and assisting with deployment using both on-premise and cloud-based platforms.

  • Develop machine learning-based software solutions using open source and proprietary software systems.
  • Conduct applied research to identify and understand different algorithms and methods for use case development.
  • Collaborate effectively within agile scrum sessions alongside the Emerging Technology, IS ML Ops teams and business stakeholders to develop and implement high-impact business solutions.
  • Rapid prototyping of new algorithms/approaches and conducting comparisons with existing algorithms and baselines.
  • Iterate on model performance through error analysis, benchmarking, feature refinement, prompt evaluation, and comparison against baseline approaches.
  • Assist the IS Infrastructure and IS ML Ops teams in designing customized ML environments as needed.
  • Support projects through the documentation, monitoring and version control of models.
  • Develop and evaluate Generative AI and Large Language Model solutions, including prompt engineering, retrieval-augmented generation, document intelligence, summarization, classification, and conversational AI use cases.
  • Work with enterprise data platforms such as Snowflake to prepare, query, transform, and analyze structured and unstructured data for AI/ML and Generative AI use cases.
  • Explore and prototype solutions using Snowflake Cortex and related cloud AI services where appropriate.

What We're Looking For

  • A completed Master's Degree in Computer Science, Statistics, Software Engineering or other STEM discipline, or equivalent working experience.
  • Experience with data collection, data annotation, and active learning.
  • Solid theoretical grounding in core machine learning concepts and techniques.
  • 2+ years of experience within a data science, artificial intelligence and/or applied machine learning position.
  • 1+ year of experience with cloud computing is an asset.
  • 1+ year of experience building production machine learning models, and deploying them to solve inference challenges at scale is an asset.
  • Strong understanding of machine learning approaches, including predictive modelling, supervised and unsupervised learning, NLP, Generative AI / Large Language Models, and model evaluation.
  • AWS Certified Machine Learning and AWS Certified Data Analytics are assets.
  • Investment Funds in Canada and/or Canadian Securities Course (CSI) is an asset.
  • 1-2 years of experience working with Snowflake, including strong SQL skills, data transformation, query optimization, and familiarity with Snowflake Cortex or other native AI/ML capabilities.
    Expands the existing Snowflake requirement.
  • Experience using Git for version control, including GitHub, branching, pull requests, and code reviews.
  • Practical experience with Generative AI / Large Language Model workflows, such as prompt engineering, retrieval-augmented generation, embeddings, vector search, model evaluation, or orchestration frameworks such as LangChain, LlamaIndex, or similar tools.
  • Familiarity with responsible AI practices, including model governance, privacy, explainability, hallucination mitigation, and secure handling of enterprise data.

The Skills You Bring

  • Strong communication skills and the ability to work with diverse stakeholders in a team environment.
  • Ability to adapt quickly in the face of change using excellent problem-solving skills and creativity.
  • Familiarity with popular Python-based AI/ML libraries, such as scikit-learn, PyTorch, pandas, NumPy, matplotlib, and associated workflows.
  • Experience with deployment of machine learning model pipelines using AWS, such as SageMaker.
  • Familiarity with containerization of ML models, including Docker and Kubernetes.
  • Strong SQL skills for querying and transforming data across cloud data platforms and relational databases, including Snowflake and traditional platforms such as Oracle, SQL Server, DB2, or MySQL.
    Replaces: "SQL skills for querying relational databases..."
  • Demonstrated proficiency with deep learning, ensemble-based methods, NLP, time series analysis, and optimization techniques.
  • Familiarity with LLM application development patterns, including prompt design, retrieval pipelines, embeddings, semantic search, and evaluation of generated outputs.
  • Ability to translate business problems into practical AI/ML or Generative AI solutions, while balancing technical feasibility, business value, and risk considerations.

Total Rewards That Reflect Your Impact

We believe exceptional work deserves exceptional recognition. That's why we offer a competitive compensation package designed to support your success today-and your financial well-being tomorrow.

For this role, your total rewards include:

  • Base Salary: A competitive annual range of $90,000 to $110,000, based on your experience and qualifications.
  • Performance Bonus: Eligible for a discretionary bonus that rewards your contributions and results.
  • RRSP Contribution: After 6 months of employment, we invest in your future with an RRSP contribution-no employee matching required.

We're proud to offer a compensation package that aligns with provincial pay transparency requirements.

This posting represents an existing vacancy within our organization-an opportunity to step into a role where your talents will make a meaningful difference.

Fidelity Canada is an equal opportunity employer

Fidelity Canada is committed to fostering a diverse and inclusive workplace. We will consider all qualified applicants for employment regardless of race, color, religion, sex, sexual orientation, gender identity or expression, national or ethnic origin, age, disability, family status, protected veterans' status, Aboriginal/Native American status or any other legally-protected ground.

Accommodation during the application process

Fidelity Canada welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in the selection process. If you require an accommodation, please email us at FidelityCanadaStaffing@fidelity.ca.

No telephone inquiries or agencies please. We thank all applicants for their interest, please be advised that only those selected for an interview will be contacted.

Why Work at Fidelity?

We are proud to be recipients of the following:

Awards

Canada's Top 100 Employers
o Greater Toronto's Top Employers
o Canada's Top Family-Friendly Employers
o Canada's Top Employers for Young People
Great Place To Work Certified
o Best Workplaces for Inclusion
o Best Workplaces for Mental Wellness
o Best Workplaces for Today's Youth
o Best Workplaces for Women
o Best Workplaces in Financial Services & Insurance
o Best Workplaces in Ontario
o Best Workplaces with Most Trusted Executive Teams

LinkedIn Top Companies in Canada
Human Resource Director (HRD) - Best Place To Work
o HRD - 5-Star Benefit Program
o HRD - 5-Star Diversity & Inclusion Employer

Designations
Canadian Compassionate Companies - Certified
Benefits Canada's Workplace Benefits Award - Future of Work Strategy
TalentEgg National Recruitment Excellence Award - Special Award for Diversity & Inclusion in Recruiting
Canadian HR Reporter's Most Innovative HR Team