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Virtual Data Labelling Jobs in New York (NOW HIRING)

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Product Content Engineer

New York, NY · On-site +1

$162K/yr

... content labeling and analysis * Experience designing and implementing evaluation frameworks ... Demonstrated data analysis skills, with experience exploring data, identifying patterns, and ...

SGS

Laboratory Technician

Fairfield, NJ · On-site

$21 - $26/hr

Review product labels for regulatory compliance * Complete accurate test reports in Microsoft word ... Online training courses, virtual and classroom development experiences, tuition reimbursement ...

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Virtual Data Labelling information

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.

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.

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 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.
What are the most commonly searched types of Data Labelling jobs in New York? The most popular types of Data Labelling jobs in New York are:
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Virtual Data Labelling jobs near you

Product Content Engineer

Instagram

New York, NY • On-site, Remote

$162K/yr

Full-time

Re-posted 22 days ago

Job description

Product Content Engineering is a horizontal function supporting initiatives across Meta's family of apps. We partner closely with product and technical teams to solve problems by providing content-centered solutions, setting standards of quality, and building the frameworks that ensure AI-powered experiences actually work for people. We're looking for a Content Engineer to join our AI Discovery team and help define how Meta evaluates and improves AI content experiences. You'll work at the intersection of content quality, AI evaluation, and the search and recommendation systems that power Meta's products: building the frameworks, rubrics, and pipelines that hold AI outputs to a high standard. You'll assess model behavior, identify where it falls short, and work cross-functionally with engineering, product, research, and data science teams to make it better.If you're energized by the opportunity to build better AI product experiences through rigorous evaluation, have experience navigating ambiguity by defining structure, prioritizing work, and driving clarity in evolving problem spaces, and apply sound editorial and analytical judgment to content quality decisions, we encourage you to apply.
Product Content Engineer Responsibilities:
  • Define content quality standards and use them to systematically evaluate how AI models are performing across our products and content experiences
  • Design golden sets, taxonomies, and guidelines that enable consistent, repeatable content quality assessments
  • Build repeatable workflows for collecting, annotating, and analyzing AI outputs so evaluations can run efficiently as models evolve
  • Evaluate successive model releases through structured comparison, documenting what improved, what regressed, and what to prioritize next
  • Design evaluation frameworks that integrate qualitative and quantitative signals to measure dimensions like user trust, content depth, and topical relevance
  • Develop processes to track content quality and model performance over time and flag regressions
  • Synthesize evaluation results into structured error patterns and concrete recommendations that engineering and product teams can act on
  • Work cross-functionally with engineers, data scientists, product managers, and content strategists to align AI behaviors with real-world user expectations

Minimum Qualifications:
  • 5+ years of experience working collaboratively with product, engineering, design, and user research teams
  • 1+ years working with generative AI products, AI evaluation, prompt engineering, annotation, and/or content labeling and analysis
  • Experience designing and implementing evaluation frameworks, annotation guidelines, or quality rubrics for AI/ML systems
  • Demonstrated data analysis skills, with experience exploring data, identifying patterns, and producing actionable insights
  • Experience building new products or platform/ecosystem products
  • Critical thinking, experience leading data-driven analyses to inform product or content decisions, and experience communicating to executive leadership
  • Proven track record of cross-functional collaboration and delivering results in environments with evolving requirements and competing priorities

Preferred Qualifications:
  • Background in content strategy, information quality, or trust and safety
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Familiarity with AI evaluation methods such as human eval, model-as-judge, A/B testing, or red-teaming
  • Experience building dashboards, scripts, or workflows that codify evaluation metrics
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Experience with Python, SQL, or other tools for data analysis and evaluation automation
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • BA or BS in Computer Science, Data Science, Linguistics, or related field

About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$162,000/year to $227,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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