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Full Time Video Annotation Jobs (NOW HIRING)

RWS Group

Data Annotator for AI Models (Italian)

$56 - $72.75/hr

... curate text, video and geographic data. This information will be used to train and improve ... Preferred : • Familiarity with the Appen Annotation Platform (ADAP) and machine learning ...

Figure

Data Quality Analyst

San Jose, CA · On-site

$30/hr

Use proprietary annotation tools to label objects, poses, and interactions in images and video ... The US base salary range for this full-time position starts at $30/hr. The pay offered for this ...

CaptionCall

Sign Language Specialist

Salt Lake City, UT · On-site

$57K - $96K/yr

... hearing! Full time Benefits * Paid Vacation Time and Paid Sick Time and Paid Holidays * 401k 6% ... Annotate sign language video content, adding linguistic labels and metadata to raw video. * Help ...

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Full Time Video Annotation information

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$26K

$59.8K

$95K

How much do full time video annotation jobs pay per year?

As of Jul 11, 2026, the average yearly pay for full time video annotation in the United States is $59,788.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,000.00 and $69,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced in a full-time video annotation role, and how can they be addressed?

A common challenge in full-time video annotation is maintaining high accuracy and consistency while labeling large volumes of video data, which can be repetitive and mentally demanding. Annotators must stay focused to avoid errors, especially when distinguishing subtle differences between objects or actions across frames. To address these challenges, it's helpful to take regular short breaks, collaborate with teammates on ambiguous cases, and make use of detailed annotation guidelines provided by the employer. Many companies also offer feedback sessions and quality assurance checks to help annotators improve their work and learn best practices.

What is a Full Time Video Annotation job?

A Full Time Video Annotation job involves watching video footage and labeling or tagging specific objects, actions, or events within the video. This work is essential for training artificial intelligence and machine learning models, especially in areas like self-driving cars, security, and content moderation. Full-time annotators are expected to maintain high accuracy and consistency while working with large volumes of data. The job may require familiarity with specialized annotation tools, attention to detail, and sometimes, a basic understanding of the domain featured in the videos.

What is the difference between Full Time Video Annotation vs Data Labeling Specialist?

AspectFull Time Video AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; training in annotation toolsHigh school diploma or equivalent; training in labeling techniques
Work EnvironmentRemote or office-based, using annotation softwareRemote or office-based, using labeling platforms
Industry UsageAutonomous vehicles, AI training, surveillanceMachine learning, AI datasets, computer vision

Full Time Video Annotation involves detailed labeling of video data for AI training, often requiring specific software skills. Data Labeling Specialists focus on annotating various data types, including images and text. Both roles are essential in AI development, but Full Time Video Annotation emphasizes video-specific tasks and tools.

What are the key skills and qualifications needed to thrive as a Full Time Video Annotation Specialist, and why are they important?

To thrive as a Full Time Video Annotation Specialist, you need strong attention to detail, familiarity with video formats, and basic computer literacy, often supported by a high school diploma or equivalent. Experience using annotation tools like CVAT, Labelbox, or VGG Image Annotator is typically required, and knowledge of data labeling standards is a plus. Excellent time management, communication skills, and the ability to focus for extended periods make someone stand out in this position. These skills and qualities are vital for producing accurate, high-quality datasets that power machine learning and AI applications.
More about Full Time Video Annotation jobs
What cities are hiring for Full Time Video Annotation jobs? Cities with the most Full Time Video Annotation job openings:
What are the most commonly searched types of Video Annotation jobs? The most popular types of Video Annotation jobs are:
What states have the most Full Time Video Annotation jobs? States with the most job openings for Full Time Video Annotation jobs include:
What job categories do people searching Full Time Video Annotation jobs look for? The top searched job categories for Full Time Video Annotation jobs are:
Full Time Video Annotation jobs near you
Infographic showing various Full Time Video Annotation job openings in the United States as of July 2026, with employment types broken down into 14% Locum Tenens, 18% Full Time, 16% Part Time, 18% Contract, 32% Nights, and 2% Summer. Highlights an 34% Physical, and 66% Remote job distribution, with an average salary of $59,788 per year, or $28.7 per hour.

Vision-Language-Action (VLA) Annotator

Objectways Technologies Llc

Phoenix, AZ • Remote

$25/hr

Full-time

Re-posted 8 days ago

Job description

Location:RemoteEmployment Type: Full-Time | 40 hours/week Compensation: $25/hour
About the Role:
We are looking for a detail-oriented and technically capable Vision-Language-Action (VLA) Annotator to join our data operations team in Phoenix, Arizona. In this role, you will be responsible for reviewing, labeling, and quality-checking multimodal datasets used to train and evaluate autonomous driving and robotics models. Your work directly impacts the safety and performance of AI systems operating in the real world.
This is a full-time, 40-hour-per-week position requiring sustained focus, sound judgment, and the ability to apply structured annotation guidelines to complex, real-world scenarios including frequent edge cases.
Key Responsibilities:
  • Review and annotate video footage, sensor telemetry, and camera feeds from autonomous vehicle test drives and robotics platforms.
  • Assess vehicle and robotic behavior in 3D space using 2D camera inputs, including approach angles, following distances, trail alignment, and controlled stop quality.
  • Use time-series telemetry data including speed, throttle, steering, and braking charts to make precise trim and segmentation decisions on data clips.
  • Apply annotation guidelines consistently while exercising independent judgment on ambiguous or edge-case scenarios.
  • Identify and flag unsafe, incomplete, or anomalous driving behaviors (e.g., rolling stops, improper following distance, out-of-distribution maneuvers).
  • Maintain high throughput and accuracy standards; participate in regular quality audits and calibration sessions.
  • Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently.
  • Document and communicate recurring issues or ambiguities in the data to improve pipeline quality.
Preferred Qualifications:
  • Education: Bachelor's degree with a STEM background preferred (Engineering, Computer Science, Physics, Mathematics, GIS, or related field).
  • Spatial & Mechanical Reasoning: Demonstrated ability to interpret vehicle or robotic behavior in 3D space from 2D camera feeds. Backgrounds in robotics, automotive engineering, mechanical engineering, GIS, or simulation are strong indicators.
  • Time-Series Data Literacy: Experience reading and interpreting sensor data, telemetry charts, lab instrumentation output, or signal processing data. Comfort with chart-heavy analytical workflows is essential for making precise trim decisions.
  • Driving Familiarity: Regular driving experience, ideally in varied or off-road conditions. Must be able to distinguish safe from unsafe driving behavior, recognize complete vs. rolling stops, and assess reasonable following distances.
  • Detail Orientation with Tolerance for Ambiguity: Ability to follow precise, rule-based guidelines while also applying sound judgment on frequent edge cases. Prior experience in QA, data annotation, or lab/research settings is a strong signal.
  • Video Review Endurance: Comfort with sustained video review tasks. Prior experience in video editing, surveillance monitoring, sports performance analysis, or media production is a plus.
Nice-To-Haves:
  • Prior annotation or data labeling experience, especially in autonomy or robotics datasets.
  • Familiarity with geospatial tools, map interfaces, or GIS platforms.
  • Hands-on experience with Encord, Label Studio, CVAT, Scale AI, or comparable labeling platforms.
  • Background in autonomous vehicles, ADAS systems, or driver safety analysis.

This is a remote position.