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Online Data Labelling Jobs in Hackensack, NJ (NOW HIRING)

Teleskope

Jr. AI Engineer - Data Annotation

New York, NY · On-site

$75K - $90K/yr

You'll do the work directly, labeling and reviewing classification data and running QC, but you won ... Familiarity with active learning or online learning approaches. * Experience with SQL and building ...

Milbank LLP

Data Security Specialist

New York, NY · On-site

$140K/yr

Implement and tune data classification, labeling, and encryption frameworks aligned with firm ... Scripting/automation proficiency (PowerShell - including Exchange Online, Compliance Center, and ...

Concord Advice

Data Scientist - Intern

Florham Park, NJ · On-site

$40K - $70K/yr

... based online lead bidding systems, omni-channel customer engagement strategies, payment ... existing labeling framework. * Support firm-wide efforts to centralize data definitions and ...

Webhosting

Senior Data Scientist, Detection

Manhattan, NY · On-site

$185 - $231/hr

Cloudflare protects and accelerates any Internet application online without adding hardware ... You make real progress with weak, delayed, or absent labels and you're energized by adversaries ...

Workila

Modellor

Jersey City, NJ · On-site

... data and make presentations to support decision-making Industry experience Banking, Credit Cards ... online by clicking on green label "I am Interested" or call if you have any question however ...

Deloitte

Cyber Data Protection Manager

Jericho, NY · On-site

$115K - $155K/yr

DLP, sensitivity labels, data classification, DSPM, DSPM for AI, on-demand classification, or ... online communications and digital products, protecting users, consumers, and patients from harm.

Deloitte

Cyber Data Protection Manager

Jersey City, NJ

$115K - $156K/yr

DLP, sensitivity labels, data classification, DSPM, DSPM for AI, on-demand classification, or ... online communications and digital products, protecting users, consumers, and patients from harm.

Deloitte

Cyber Data Protection Manager

Morristown, NJ · On-site

$114K - $154K/yr

DLP, sensitivity labels, data classification, DSPM, DSPM for AI, on-demand classification, or ... online communications and digital products, protecting users, consumers, and patients from harm.

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All JobsOnline Data Labelling JobsOnline Data Labelling Jobs in Hackensack, NJ

Online Data Labelling information

See Hackensack, NJ salary details

$50.2K

$180K

$265.6K

How much do online data labelling jobs pay per year?

As of Aug 24, 2026, the average yearly pay for online data labelling in Hackensack, NJ is $179,977.00, according to ZipRecruiter salary data. Most workers in this role earn between $145,600.00 and $185,400.00 per year, depending on experience, location, and employer.

What is online data labelling?

Online data labelling is the process of tagging or annotating data—such as images, text, or audio—using digital tools to make it understandable for machine learning algorithms. Data labelers review raw data and apply predefined labels to help train artificial intelligence systems, enabling them to recognize patterns and make predictions. This work is essential for improving the accuracy and performance of AI models in various applications, such as image recognition, natural language processing, and autonomous vehicles. Online data labelling jobs are often remote and require attention to detail, consistency, and sometimes domain-specific knowledge.

What are the key skills and qualifications needed to thrive as an online data labeller?

To excel as an Online Data Labeller, you need strong attention to detail, basic data handling skills, and familiarity with data annotation protocols, often requiring at least a high school diploma. Proficiency with data labelling platforms such as Labelbox, Supervisely, or Scale AI, and sometimes knowledge of spreadsheet tools, is typically necessary. Reliability, consistency, and the ability to follow detailed guidelines make individuals stand out in this role. These skills ensure high-quality, accurately labelled datasets that are critical for training effective AI and machine learning models.

What are some common challenges faced by online data labellers, and how can they be managed effectively?

Online data labelers often encounter challenges such as repetitive tasks, strict accuracy requirements, and tight deadlines. Maintaining high attention to detail is crucial, as even small errors can impact the quality of machine learning models. To manage these challenges, it's helpful to take regular breaks, use productivity tools, and communicate any ambiguities or unclear instructions with supervisors or team leads. Many organizations also offer support channels and quality assurance feedback to help labelers continuously improve their work.

What is the difference between Online Data Labelling vs Data Annotation?

AspectOnline Data LabellingData Annotation
CredentialsBasic computer skills, attention to detailSimilar, often no formal certification required
Work EnvironmentRemote, flexibleRemote or in-office, depending on project
Industry UsageCommon in AI/ML data preparationUsed across AI, computer vision, NLP projects
Search IntentOnline Data Labelling vs Data Annotation

Online Data Labelling and Data Annotation are closely related roles in AI data preparation. While both involve labeling data for machine learning, Online Data Labelling often emphasizes quick, online tasks, whereas Data Annotation may include more detailed, specialized labeling. Both roles are essential in training AI models and share similar skills and work environments.

Online Data Labelling jobs near you

Jr. AI Engineer - Data Annotation

Teleskope

New York, NY • On-site

$75K - $90K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 4 days ago

Job description

About Teleskope
Teleskope is redefining data security for the AI era with the only dedicated platform that combines precise visibility with automated remediation. Teleskope continuously scans, catalogs, and classifies data in-motion and at-rest while automating policy-based actions, helping organizations proactively manage data sprawl while securely enabling AI adoption.
Fresh off our $25 million Series A round, Teleskope is entering a high-growth phase backed by top-tier investors and exceptional product-market fit.
About the Role
We're looking for a hungry, hands-on AI Engineer to join our data science team. You'll do the work directly, labeling and reviewing classification data and running QC, but you won't just execute. You'll bring an engineer's mindset to it: when a task is repetitive, you script it; when quality is hard to measure, you build a way to measure it. You'll use Python, SQL, and agentic development tools to make annotation and QC faster, more consistent, and more scalable.
This is a rapidly evolving role, and we expect you to context switch comfortably as priorities shift. You'll work shoulder-to-shoulder with data scientists and ML engineers, people who think about data the way you do, and the labels and quality signals you produce feed directly into the models that protect real customers' most sensitive data. The work is high-impact and the data is messy; a big part of the job is learning, through the work itself, what it takes to make it usable.
This is a hybrid role requiring 3+ days in-office in New York City.
Who Should Apply
We're looking for someone with programming ability, dependability, and the drive to learn on the job. Recent grads are welcome, and CS and STEM backgrounds are a great fit. What matters most is that you can think critically, you're excited to work through messy data, you can context switch as priorities change, and you want to grow fast in a fast-moving environment.
What You'll Do
  • Do hands-on data annotation and quality control (labeling, reviewing, and correcting classification outputs) as a core member of the data science pipeline.
  • Take ownership of improving and scaling the process: find the bottlenecks, repetitive steps, and sources of error, and fix them with Python, SQL, and agentic workflows.
  • Build and run quality control checks that catch labeling errors, measure inter-annotator agreement, and surface systematic issues before they reach production.
  • Work closely with data scientists and ML engineers to close the loop between real-world performance and model improvement.
  • Context switch across labeling, quality analysis, scripting, and process work as priorities evolve.
  • Document QC processes and annotation guidelines to support team scaling and onboarding.
About You
  • Solid programming ability, with hands-on Python experience and a willingness to dig into scripts, SQL, and data wrangling.
  • Comfortable using agentic development tools, or eager to ramp up on them fast.
  • A quality-first mindset. You notice when something is off in the data and won't let it slide.
  • Dependable and adaptable. Teammates can count on you, and you stay effective as priorities shift.
  • Energized by messy, real-world data and by working alongside other data-minded people.
  • Hungry, self-directed, and ready to grow with Teleskope as we scale.
Nice to Have
  • Familiarity with feedback loops in ML systems and how label quality connects to model performance.
  • Experience with annotation platforms (Label Studio, Prodigy, Scale, or custom-built systems).
  • Familiarity with active learning or online learning approaches.
  • Experience with SQL and building lightweight dashboards to track quality metrics.
  • Background in NLP or text classification workflows.
What You'll Get
  • A seat alongside data scientists and ML engineers, data-minded people to learn from every day.
  • Work that visibly matters. Your labels feed the models that protect real customers' most sensitive data.
  • Ownership of the annotation and quality processes that determine classification accuracy across the platform.
  • Room to grow fast, with real ownership from day one as Teleskope scales.
  • A beautiful, well-stocked office in NYC's Financial District.
  • Flexible vacation and work-from-home days.
  • Competitive salary and meaningful equity.
  • Health, vision, dental, 401k, and more benefits, heavily subsidized by Teleskope.
What We Value
At Teleskope, we value builders who care about the details. This role is for someone who sees data quality not as a support function but as a force multiplier, and who takes pride in making the people around them more effective. We look for dependable teammates who ship iteratively, take ownership, and understand that great ML starts with great data.

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