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Online 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 ... Ownership of the annotation and quality processes that determine classification accuracy across the ...

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How much do online annotation jobs pay per year?

As of Jul 27, 2026, the average yearly pay for online annotation in the United States is $45,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,500.00 and $47,500.00 per year, depending on experience, location, and employer.

What is an Online Annotation job?

An Online Annotation job involves labeling or tagging data such as images, text, audio, or video to help train machine learning models. Annotators follow specific guidelines to ensure accuracy and consistency in the data. These jobs are crucial in AI development, improving the quality of automated systems like image recognition, speech processing, and natural language understanding. Many online annotation jobs are remote and require attention to detail, basic computer skills, and sometimes domain-specific knowledge.

What are some common challenges faced by Online Annotation professionals, and how can they be managed?

A common challenge in Online Annotation is maintaining accuracy and consistency across large volumes of data, especially when guidelines are complex or frequently updated. To address this, professionals often need to regularly review instructions and participate in feedback sessions or quality audits provided by their employer. Staying organized, managing workload efficiently, and asking for clarification when uncertain can help minimize errors. Employers may provide training sessions or support forums to help annotators improve their skills and keep up-to-date with best practices.

What are the key skills and qualifications needed to thrive in the Online Annotation position, and why are they important?

To excel in Online Annotation, strong attention to detail, proficiency in data labeling, and a solid understanding of guidelines for data quality are essential, often requiring at least a high school diploma or equivalent. Familiarity with annotation platforms, database tools, and sometimes basic knowledge of machine learning concepts is advantageous. Reliability, time management, and the ability to follow specific instructions set apart top performers in this role. These skills are crucial for providing high-quality, consistent data that supports training and validation of AI systems.

More about Online Annotation jobs
What cities are hiring for Online Annotation jobs? Cities with the most Online Annotation job openings:
What are the most commonly searched types of Annotation jobs? The most popular types of Annotation jobs are:
What states have the most Online Annotation jobs? States with the most job openings for Online Annotation jobs include:
What job categories do people searching Online Annotation jobs look for? The top searched job categories for Online Annotation jobs are:
Online Annotation jobs near you
Infographic showing various Online Annotation job openings in the United States as of July 2026, with employment types broken down into 37% Locum Tenens, 23% Full Time, 28% Part Time, 1% Contract, 10% Nights, and 1% Summer. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution, with an average salary of $45,000 per year, or $21.6 per hour.
Jr. AI Engineer - Data Annotation

Jr. AI Engineer - Data Annotation

Teleskope

New York, NY • On-site

$75K - $90K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 7 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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