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Remote Video Labelling Jobs in Phoenix, AZ (NOW HIRING)

All JobsVideo Labelling JobsRemote Video Labelling JobsRemote Video Labelling Jobs in Phoenix, AZ

Remote Video Labelling information

See Phoenix, AZ salary details

$37.7K

$75K

$128.1K

How much do remote video labelling jobs pay per year?

As of Jul 13, 2026, the average yearly pay for remote video labelling in Phoenix, AZ is $74,963.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,600.00 and $86,900.00 per year, depending on experience, location, and employer.

How much are data labelers paid?

Data labelers, including those working remotely in video labeling roles, typically earn between $10 and $20 per hour depending on experience, complexity of tasks, and the company. Pay rates can vary based on the platform, project scope, and whether the work is freelance or full-time employment.

How can I make 2000 a week working from home?

Remote video labelling jobs can pay varying rates, often between $10 and $20 per hour, depending on the company and project complexity. To earn $2,000 weekly, you would need to work approximately 100 hours at these rates, which may require high-volume or premium projects, strong attention to detail, and efficient use of annotation tools. Building experience and a good reputation can help access higher-paying opportunities in this field.

What is remote video labelling?

Remote video labelling is the process of watching video footage and accurately annotating or tagging objects, actions, or events within the video, all while working from a remote location, usually from home. This work is essential for training machine learning and AI models, particularly in fields like autonomous vehicles, security, and content moderation. Video labellers use specialized software to mark frames and provide metadata that helps computers understand visual information. Attention to detail and consistency are crucial in this job to ensure high-quality labelled data.

What are the key skills and qualifications needed to thrive as a Remote Video Labelling Specialist, and why are they important?

To thrive as a Remote Video Labelling Specialist, attention to detail, basic computer proficiency, and a high school diploma or equivalent are generally required. Familiarity with annotation tools, video editing software, and data labeling platforms is typically expected, with some roles preferring experience in machine learning or data management systems. Strong time management, focus, and effective communication skills help individuals excel in independent, deadline-driven environments. These skills ensure accurate data labeling, which is crucial for training high-quality AI and machine learning models.

Is data labeling work from home?

Remote video labelling jobs are often performed from home, allowing workers to complete tasks using a computer and internet connection. These roles typically require attention to detail, familiarity with labeling tools, and a flexible schedule, making them suitable for remote work environments.

What is a video labeling job?

A video labeling job involves reviewing and annotating video content to help train machine learning algorithms. Workers typically use specialized tools to add tags, identify objects, or categorize scenes, often working remotely with flexible schedules. Accuracy and attention to detail are important for this type of data annotation work.

What are some common challenges faced by remote video labelling professionals, and how can they be managed?

Remote video labelling professionals often encounter challenges such as staying focused during repetitive tasks, ensuring accuracy when identifying subtle visual details, and managing communication with team members across different time zones. To address these, it's helpful to set up a distraction-free workspace, take regular breaks to maintain concentration, and use collaborative tools to stay connected with supervisors and peers. Additionally, following established labelling guidelines and participating in quality assurance sessions can help maintain consistency and accuracy in your work.

What is the difference between Remote Video Labelling vs Remote Image Annotation?

AspectRemote Video LabellingRemote Image Annotation
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote, flexible hours
Industry UsageAutonomous vehicles, surveillance, AI trainingObject detection, medical imaging, retail
Search & Comparison IntentUnderstanding differences in data labeling rolesUnderstanding differences in annotation tasks

Remote Video Labelling involves annotating video data frame-by-frame, often requiring temporal consistency, while Remote Image Annotation focuses on labeling individual images. Both roles are remote, require attention to detail, and are used in AI training across various industries. The main difference lies in the data type: videos versus images, with video labelling demanding more complex, time-sensitive annotations.

What are popular job titles related to Remote Video Labelling jobs in Phoenix, AZ? For Remote Video Labelling jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Remote Video Labelling jobs in Phoenix, AZ look for? The top searched job categories for Remote Video Labelling jobs in Phoenix, AZ are:
What cities near Phoenix, AZ are hiring for Remote Video Labelling jobs? Cities near Phoenix, AZ with the most Remote Video Labelling job openings:
Remote Video Labelling jobs near you

Vision-Language-Action (VLA) Annotator

Objectways Technologies Llc

Phoenix, AZ • Remote

$25/hr

Full-time

Re-posted 11 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.