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Seasonal 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 ...

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Seasonal Online Data Annotation information

What is a seasonal online data annotation job?

Seasonal online data annotation jobs involve labeling or categorizing data—such as images, text, or audio—for machine learning purposes during specific peak periods. These roles are typically temporary and align with times of increased demand, such as during product launches or holidays. Data annotators work remotely to identify and tag relevant features in datasets, helping companies train and improve AI systems. No advanced technical skills are usually required, but attention to detail and reliability are important. Many companies hire seasonal annotators to quickly scale up their data processing capabilities for short-term projects.

What are the key skills and qualifications needed to thrive as a seasonal online data annotation specialist?

A strong attention to detail, proficiency in data entry, and basic computer literacy are essential for success as a Seasonal Online Data Annotation specialist, typically requiring at least a high school diploma. Familiarity with annotation platforms, spreadsheets, and quality assurance tools is often expected. Excellent time management, adaptability, and communication skills help individuals work efficiently and collaborate remotely. These abilities ensure high-quality, accurate data labeling that directly impacts machine learning and AI model performance.

What are some common challenges faced in a seasonal online data annotation role, and how can I prepare for them?

One of the main challenges in a Seasonal Online Data Annotation role is maintaining consistent accuracy and attention to detail over long periods of repetitive work. Deadlines can be tight, especially during peak project periods, making time management crucial. Additionally, guidelines for annotation tasks may change or require frequent clarification, so adaptability and clear communication with supervisors are important. To prepare, familiarize yourself with annotation tools, practice following detailed instructions, and develop strategies to stay focused and motivated during repetitive tasks.

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

AspectSeasonal Online Data AnnotationData Labeling Specialist
CredentialsBasic computer skills, attention to detailSimilar, often no formal certification required
Work EnvironmentRemote, flexible hours during peak seasonsRemote or on-site, consistent work environment
Industry UsageUsed mainly in AI training for seasonal projectsUsed broadly across AI, machine learning, and data science
Search & Comparison IntentUnderstanding seasonal data annotation rolesUnderstanding general data labeling roles

Seasonal Online Data Annotation involves temporary, project-based tasks focused on annotating data for specific seasonal AI applications. Data Labeling Specialists perform ongoing data annotation tasks across various projects. While both roles require attention to detail and similar skills, Seasonal Online Data Annotation is typically temporary and project-specific, whereas Data Labeling Specialists often have more consistent, long-term roles.

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What job categories do people searching Seasonal Online Data Annotation jobs look for?

The top searched job categories for Seasonal Online Data Annotation jobs are:

Seasonal Online Data Annotation jobs near you
Infographic showing various Seasonal Online Data Annotation job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Jr. AI Engineer - Data Annotation

Teleskope

New York, NY • On-site

$75K - $90K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 27 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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