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Virtual Data Labelling Jobs (NOW HIRING)

Meta

Director, Data Licensing Partnerships

Menlo Park, CA · On-site

$296K - $356K/yr

... data labeling partners. • Lead complex commercial and technical negotiations, structuring ... Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual ...

Diverse Lynx

Lead Data Center Technician

Dallas, TX · On-site

They operate in tight spaces, run & label cabling, and improve physical security around their ... They also create concise virtual information reports to keep company management informed of status ...

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Virtual Data Labelling information

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$165K

$243.5K

How much do virtual data labelling jobs pay per year?

As of Jun 1, 2026, the average yearly pay for virtual data labelling in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Virtual Data Labeller, and why are they important?

To thrive as a Virtual Data Labeller, you need strong attention to detail, accuracy, and basic data processing skills, typically supported by a high school diploma or relevant experience. Familiarity with data annotation tools, content management systems, and sometimes basic programming or spreadsheet software is important. Strong time management, focus, and effective communication skills help you meet deadlines and collaborate with remote teams. These abilities are crucial to ensure high-quality, consistent data labelling that directly impacts the performance of machine learning models.

How does a virtual data labeller typically collaborate with data scientists and machine learning engineers?

Virtual data labellers play a crucial role in supporting data scientists and machine learning engineers by accurately tagging data that will be used to train and validate models. Collaboration often occurs through project management tools or direct communication platforms, where labellers receive guidelines and feedback to ensure consistency and quality. Regular check-ins or quality audits are common, and labellers may join virtual meetings to clarify requirements or discuss ambiguous cases. This teamwork helps ensure that the labelled data meets project standards and contributes to the success of AI initiatives.

What is virtual data labelling?

Virtual data labelling is the process of annotating or tagging data, such as images, videos, or text, through online platforms to make it understandable for machine learning algorithms. Data labelers work remotely to identify and categorize objects, features, or information within datasets, which helps train artificial intelligence systems. This job is essential in industries like autonomous vehicles, healthcare, and e-commerce, where large volumes of labelled data are needed to improve AI accuracy.

What is the difference between Virtual Data Labelling vs Data Annotation Specialist?

AspectVirtual Data LabellingData Annotation Specialist
CredentialsBasic computer skills, training in labelling toolsSimilar, often requires training in annotation software
Work EnvironmentRemote, online platformsRemote or on-site, depending on employer
Industry UsageAI, machine learning, autonomous vehiclesAI, computer vision, NLP projects
Search IntentLabeling data for AI modelsAnnotating data for machine learning

Both roles involve preparing data for AI systems, but Virtual Data Labelling focuses on assigning labels to datasets using online tools, while Data Annotation Specialists may perform more detailed annotations, often requiring specific domain knowledge. Both are essential in AI development and share similar work environments and skill requirements.

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Virtual Data Labelling jobs near you

Quality Assurance Engineer - AV Behavior Simulation Testing

Avride

Austin, TX • On-site

Other

Posted 7 days ago

Job description

About the Team

Our Simulation Testing team ensures reliability and safety of autonomous driving systems through comprehensive virtual scenario testing. We collaborate closely with the Motion Planning, Prediction, Perception, and Control teams to build effective offline testing environments, reliable testing processes, and datasets. Our main goal is to detect and resolve issues as early and thoroughly as possible, prior to testing in real-world conditions.

About the Role

As a QA Engineer in Simulation Testing, you'll design, execute, and analyze virtual test scenarios, focusing on validation of autonomous driving software. You'll work closely with development, analytics, and data labeling teams, prepare checklists and test scenarios, gather testing datasets, and deliver clear and actionable testing reports. Your analytical mindset and proactive approach to continuous improvement will be key to your success in this role.

What You'll Do
  • Design and implement structured testing plans for new features and bug fixes
  • Develop detailed checklists and testing scenarios
  • Collect and organize datasets for thorough testing
  • Analyze test outcomes and document defects clearly
  • Create reports on test findings and safety metrics
  • Design metrics for evaluating AV performance and safety
  • Collaborate closely with cross-functional teams to improve test coverage and effectiveness
What You'll Need
  • 3+ years of experience in software testing
  • Strong analytical abilities and structured thinking
  • Proactive, inquisitive mindset with a drive for continuous improvement and deeper product understanding
  • Excellent ability to plan and prioritize tasks effectively under varying workloads
  • Good understanding of traffic rules and driving regulations
Nice to Have
  • Experience with autonomous vehicle testing
  • Proficiency in at least one programming language (e.g., Python)
  • Basic understanding of vehicle dynamics or sensor technology (LiDAR, Radar, Cameras)

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