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Video Annotator Jobs (NOW HIRING)

Innodata Inc.

QA / Evaluation Lead

Washington, DC · On-site

$45 - $50/hr

Design and own the inter-annotator agreement (IAA) methodology for the Phase 1 demonstration corpus ... Experience with FMV / video annotation quality standards The expected hourly salary range for this ...

Innodata Inc.

$45 - $50/hr

Design and own the inter-annotator agreement (IAA) methodology for the Phase 1 demonstration corpus ... Experience with FMV / video annotation quality standards The expected hourly salary range for this ...

Prointegrate LLC

Be Seen First

AI Video Data Annotator(50-90 USD per hour) Remote, 1000 Openings

OR · On-site

$50 - $90/hr

Dear Applicant, Thank you for your interest in Video AI Data Annotation Role (50-90 USD per hour) (Apply directly using below job link) Only applicants through link will be considered for this fast ...

Prointegrate LLC

Be Seen First

AI Video Data Annotator(50-90 USD per hour) Remote, 1000 Openings

OR · On-site

$50 - $90/hr

Dear Applicant, Thank you for your interest in Video AI Data Annotation Role (50-90 USD per hour) (Apply directly using below job link) Only applicants through link will be considered for this fast ...

Ursus, Inc.

Strategic Project Lead, DataFactory

... inter-annotator consistency, and preference modeling. • Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio) • Technical fluency in data ...

Nvidia

Engineering Manager, Data Labeling Platform

... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:

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Video Annotator information

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$22

$28

$33

How much do video annotator jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for video annotator in the United States is $28.92, according to ZipRecruiter salary data. Most workers in this role earn between $26.44 and $30.53 per hour, depending on experience, location, and employer.

What is a video annotator?

Video annotators are professionals who label, tag, or mark specific objects, actions, or events within video footage. Their work is essential for training machine learning models, particularly in computer vision tasks like object detection, tracking, and activity recognition. Video annotators use specialized software tools to frame-by-frame identify and classify elements, ensuring data accuracy for AI applications such as autonomous vehicles, security surveillance, and sports analytics.

What are the key skills and qualifications needed to thrive as a video annotator, and why are they important?

To thrive as a Video Annotator, you need attention to detail, strong visual perception, and familiarity with annotation guidelines, typically supported by a high school diploma or relevant experience. Proficiency in annotation software such as CVAT, Labelbox, or VGG Image Annotator, as well as basic computer skills, is often required. Strong communication, time management, and the ability to follow instructions precisely make someone stand out in this position. These skills ensure high-quality, consistent data labeling that is critical for training accurate machine learning and AI models.

What are some common challenges faced by video annotators and how can they be managed?

Video Annotators often encounter challenges such as maintaining high accuracy while labeling large volumes of video data and handling repetitive tasks that can lead to fatigue. Additionally, interpreting ambiguous scenes or adhering to nuanced annotation guidelines can be tricky. To manage these challenges, it's important to take regular breaks, maintain open communication with team leads for clarification, and use annotation tools efficiently. Collaborating with peers and participating in quality assurance reviews can also help ensure consistency and accuracy in the annotations.

What is the difference between Video Annotator vs Video Labeler?

AspectVideo AnnotatorVideo Labeler
CredentialsBasic computer skills, attention to detailSimilar credentials, often with familiarity in labeling tools
Work EnvironmentRemote or on-site, working with video dataSimilar, often in data annotation teams
Industry UsageMedia, AI training, content moderationAI development, machine learning datasets
Job FocusAnnotating video content, drawing bounding boxes, taggingLabeling video segments, categorizing actions

Video Annotators and Video Labelers perform closely related tasks in video data preparation for AI and machine learning. While both roles involve working with video content, Video Annotators focus on detailed annotation like bounding boxes and tagging, whereas Video Labelers typically categorize and segment videos for training datasets. Both roles require similar skills and are often found in AI, media, and tech industries.

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What are popular job titles related to Video Annotator jobs?

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Video Annotator jobs near you
Infographic showing various Video Annotator job openings in the United States as of September 2026, with employment types broken down into 42% Full Time, 25% Part Time, and 33% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $60,160 per year, or $28.9 per hour.

Senior Annotation and Data Pipeline Manager

San Francisco, CA • On-site

Genesis AI
See more jobs at Genesis AIexternal

Other

Posted 11 days ago

Job description

The role

We have built a frontier model and put Eno in front of the world, fast. Behind that is a data engine: the machine that turns a raw human demonstration into data the model is measurably better for. This role owns that engine.

A worn glove and a camera produce a raw demonstration, not training data. You will build the pipeline and the annotation operation that turn raw demonstrations into clean, labeled, training-ready data, and make it scale with automation rather than headcount. You will own the datasets, what gets annotated, and the ontology, how it gets labeled, bring vision-language models to bear on trajectory labeling and language grounding, and close the loop so the engine keeps making the model better. This role serves the whole operation, our own floors and our partner-funded collection.

What you'll do

  • Run the data engine. Own the loop from raw trajectory and video to training-ready datasets, with validation steps that guarantee clean, correctly labeled data.

  • Own datasets and ontology. Decide what gets annotated and how, designing the ontology with the model team for its training implications.

  • Automate with models. Use vision-language models for automated trajectory annotation, language grounding, and data synthesis, so the pipeline scales without linear headcount, while holding the quality bar.

  • Run the annotation operation. Stand up and scale labeling, internal and vendor, against a clear quality bar and a delivery schedule the model team can plan around.

  • Close the loop. Turn real-robot eval failures into targeted collection and annotation jobs, and prove the new data improves the model.

  • Own the metrics. Track inter-annotator agreement, label error rate, and throughput per annotator-hour, and drive them the right way.

What we're looking for

  • You have scaled an annotation or data pipeline at a serious operation. Four or more years in data or ML pipelines, including time leading the work. At a frontier AI lab or a top data operation, you have taken raw robot or embodied data to training-ready at volume and you know exactly where it breaks. The people who have done this are a small group. If you are one, we want to talk.

  • You can build, not just manage. Strong Python (Pandas, NumPy, PyTorch) and SQL. You write the automation that shrinks the pipeline.

  • ML literacy. You understand training versus test, precision and recall, and overfitting well enough to design an ontology that helps the model, not just labels data.

  • Hands-on technical leadership. You can run a labeling operation and stay a hands-on contributor at the same time.

  • Comfortable with ambiguity and speed. You move fast in a research-paced environment and bring order to it.

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