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Internship Remote Data Labelling Jobs in Toronto, ON

Remote (with hybrid options in Toronto and Chicago only) Compensation: Hourly ($32) About Us : At ... Analyze financial data to identify trends, variances, and opportunities for cost savings and ...

Remote (with hybrid options in Toronto) Compensation: Hourly ($32) The Vosyn internship is unique ... Work with data structures (e.g., arrays, linked lists, trees, graphs) and implement algorithms for ...

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Internship Remote Data Labelling information

What are the key skills and qualifications needed to thrive as an Internship Remote Data Labelling professional, and why are they important?

To excel as an Internship Remote Data Labelling professional, you need strong attention to detail, basic computer literacy, and familiarity with data annotation processes, often requiring at least a high school diploma or equivalent. Experience with data labelling platforms such as Labelbox or Supervisely, and understanding file formats like CSV or JSON, are commonly expected. Reliability, time management, and effective communication are important soft skills for remote collaboration and meeting deadlines. These competencies ensure high-quality, consistent data labelling that supports accurate machine learning model development.

What is the difference between Internship Remote Data Labelling vs Data Annotation Specialist?

AspectInternship Remote Data LabellingData Annotation Specialist
CredentialsTypically students or entry-level with basic computer skillsOften requires experience or training in data annotation tools
Work EnvironmentRemote, flexible hours, internship settingRemote or on-site, professional setting
Employer & IndustryTech companies, AI startups, research projectsAI, machine learning, data services companies
Search & Comparison IntentLearning opportunity, entry-level roleProfessional data labeling work, career development

Internship Remote Data Labelling typically involves entry-level, temporary roles focused on training and learning, often suitable for students. Data Annotation Specialists are more experienced professionals performing detailed labeling tasks for ongoing projects. While both roles involve data labeling, the internship emphasizes skill development, whereas the specialist role centers on professional expertise.

What are some typical challenges faced by remote data labelling interns, and how can they be addressed?

Remote data labelling interns often encounter challenges such as managing repetitive tasks, maintaining high accuracy, and communicating effectively with team members across different time zones. To address these, it's helpful to establish a structured daily routine, regularly review quality guidelines, and use collaboration tools like Slack or Teams to stay connected. Seeking timely feedback from supervisors and participating in virtual team check-ins can also improve both efficiency and data consistency.

What is an Internship Remote Data Labelling job?

An Internship Remote Data Labelling job involves reviewing and tagging data—such as images, text, or audio—from a remote location to help train machine learning algorithms. Interns in this role classify, annotate, or categorize raw data according to specific guidelines provided by the employer or project. This work is crucial for improving the accuracy of AI models, as properly labeled data enables better learning outcomes. Remote data labelling internships are ideal for students or recent graduates looking to gain experience in AI, data science, or related fields while working from anywhere.
What job categories do people searching Internship Remote Data Labelling jobs in Toronto, ON look for? The top searched job categories for Internship Remote Data Labelling jobs in Toronto, ON are:
Infographic showing various Internship Remote Data Labelling job openings in Toronto, ON as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.
Healthcare Workflow Specialist - Fully Remote | Upto $80/hr

Healthcare Workflow Specialist - Fully Remote | Upto $80/hr

Mercor

Toronto, ON • Remote

CA$80/hr

Full-time

Posted 14 days ago


Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Registered Nurses (Data labelling and training)
Type: Contract
Compensation: $60–$80/hour
Location: Remote

Role Responsibilities

  • Review and assess clinical documentation for accuracy, completeness, and quality.
  • Analyze healthcare workflows and recommend process improvements.
  • Interpret patient records and clinical notes to support product development.
  • Identify documentation gaps, inconsistencies, and edge cases.
  • Collaborate with product, engineering, and operations teams to refine AI-driven healthcare tools.
  • Ensure adherence to healthcare regulations, policies, and best practices.

Qualifications

Must-Have

  • Active Registered Nurse (RN) license.
  • 3–5+ years of clinical nursing experience.
  • Strong understanding of clinical documentation and healthcare workflows.
  • Experience working with EHR systems (Epic, Cerner, Meditech, etc.).
  • Excellent analytical, communication, and problem-solving skills.

Preferred

  • Experience in CDI, case management, utilization review, or quality improvement.
  • Exposure to healthcare technology, AI tools, or digital health products.
  • Ability to collaborate effectively with cross-functional teams.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.