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Internship Online Data Annotation Jobs (NOW HIRING)

Teleskope

Jr. AI Engineer - Data Annotation

New York, NY · On-site

$75K - $90K/yr

Familiarity with active learning or online learning approaches. * Experience with SQL and building ... most sensitive data. * Ownership of the annotation and quality processes that determine ...

Doist

Senior Data Operations Engineer

Boston, MA · On-site

$120 - $180/hr

Use hands‑on collection and annotation work to surface tooling gaps and drive the tooling roadmap above. Team Management & Documentation -- 25% * Manage and schedule data collection interns and ...

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FieldAI

Senior Data Operations Engineer

Boston, MA · On-site

$110K - $140K/yr

Use hands-on collection and annotation work to surface tooling gaps and drive the tooling roadmap above. Team Management & Documentation - 25% * Manage and schedule data collection interns and ...

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How much do internship online data annotation jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for internship online data annotation in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is an internship online data annotation?

Internship Online Data Annotation positions are temporary roles, often held by students or recent graduates, where individuals label and categorize data such as images, text, or audio for use in machine learning and artificial intelligence projects. Interns work remotely or in-office to ensure data is accurately annotated according to project guidelines, helping to train and improve AI models. These internships provide hands-on experience in data management, attention to detail, and exposure to AI technologies. They are ideal for those interested in technology, data science, or AI fields, and can be a stepping stone to more advanced roles.

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

To thrive as an Internship Online Data Annotation, you generally need attention to detail, basic computer skills, and a high school diploma or equivalent. Familiarity with annotation platforms, data labeling tools, and sometimes Excel or similar spreadsheet software is typically required. Strong communication, reliability, and the ability to follow precise instructions make candidates stand out in this role. These skills ensure high-quality, accurate data labeling, which is vital for training reliable machine learning models.

What types of data and tools will I typically work with during an internship online data annotation?

As an Online Data Annotation intern, you'll commonly work with a variety of data types such as text, images, audio, or video, depending on the project's focus. You'll use specialized annotation platforms or software to label and categorize data accurately for use in machine learning models. Interns often collaborate with data scientists and engineers to understand project requirements and ensure high-quality outputs. Attention to detail and following precise guidelines are key challenges in this role, but you'll gain valuable exposure to real-world AI development workflows.

What is the difference between Internship Online Data Annotation vs Data Labeling Specialist?

AspectInternship Online Data AnnotationData Labeling Specialist
CredentialsTypically students or entry-level with basic computer skillsUsually requires experience or training in data labeling tools
Work EnvironmentRemote, flexible, often part-timeRemote or on-site, depending on employer
Industry UsageCommon in AI/ML projects, tech companiesUsed in AI, autonomous vehicles, healthcare, and more

Internship Online Data Annotation roles are often entry-level, focusing on training and learning, while Data Labeling Specialists are more experienced, handling complex labeling tasks. Both roles are essential in AI development but differ in experience requirements and scope.

Can I work for internship online data annotation with no experience?

Internship online data annotation roles often do not require prior experience, as training is typically provided to help new workers learn annotation tools and standards. Basic computer skills and attention to detail are usually sufficient to start, making it accessible for beginners. However, some positions may prefer or require familiarity with specific data types or annotation software.

Does data annotation really pay you?

Data annotation jobs, including online data annotation internships, typically pay hourly or per task rates, with earnings varying based on the platform, complexity of tasks, and experience. While some positions offer competitive pay, others may provide lower wages, and payment is usually processed through online payment systems like PayPal or direct deposit. It is important to verify the payment terms before starting any data annotation work.
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Internship Online Data Annotation jobs near you
Infographic showing various Internship Online Data Annotation job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Jr. AI Engineer - Data Annotation

Teleskope

New York, NY • On-site

$75K - $90K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted yesterday

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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