1

Video Labelling Jobs in California (NOW HIRING)

Data collection, labeling (manual and automated), training, evaluation, deployment, monitoring. * Stream and process video at industrial volume. Multi-camera setups, real-time inference on the edge ...

Data and Perception Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Experience with ergonomics, pose estimation, action recognition, or video understanding. * Experience with annotation platforms (Scale, Labelbox, V7, internal tools) and building automated labeling ...

Familiarity with large-scale crawling of multimodal data and the associated challenges of video processing, codecs, and compression. * Taxonomy Design: Experience in designing complex labeling ...

Partner with label digital teams to ensure campaigns are innovative and platform‑native; focus on video strategy and discovery. * Liaise directly with labels, managers, and artists to guide YouTube ...

Showing results 21-40

Video Labelling information

What is a video labelling?

A Video Labelling job involves annotating or tagging objects, actions, or events in video footage to train machine learning models. This process helps AI systems recognize and interpret visual data accurately. Tasks may include drawing bounding boxes, classifying scenes, or adding timestamps for specific events. Video labelling is commonly used in industries like autonomous driving, security surveillance, and content moderation.

What does a video labelling do?

A typical day in Video Labelling involves reviewing video footage, identifying and annotating specific objects or events according to project guidelines, and entering this data into specialized software tools. Team members often collaborate with data scientists, engineers, or quality assurance leads to ensure accuracy and consistency in the annotations. Depending on the project and employer, you may work independently or as part of a larger team, sometimes with set quotas or deadlines. This work is crucial for developing and refining AI and machine learning models, making attention to detail and adherence to standards especially important. Over time, experienced video labelling professionals may progress to quality assurance roles or team leads overseeing larger annotation projects.

What are the key skills and qualifications needed to thrive in video labelling, and why are they important?

To thrive as a Video Labelling professional, you should have excellent attention to detail, basic computer proficiency, and familiarity with visual content analysis. Knowledge of annotation platforms, video editing software, or AI training tools is often required, and experience with data labelling systems can be beneficial. Strong communication, reliability, and the ability to follow detailed guidelines are important soft skills for this role. These abilities ensure high-quality, consistent data annotation that directly supports machine learning and computer vision projects.

What are the most commonly searched types of Video Labelling jobs in California?

The most popular types of Video Labelling jobs in California are:

What are popular job titles related to Video Labelling jobs in California?

For Video Labelling jobs in California, the most frequently searched job titles are:

What job categories do people searching Video Labelling jobs in California look for?

The top searched job categories for Video Labelling jobs in California are:

What cities in California are hiring for Video Labelling jobs?

Cities in California with the most Video Labelling job openings:

Infographic showing various Video Labelling job openings in California as of August 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 94% In-person, and 6% Remote job distribution.

Data Annotation Specialist Redwood City, CA

Redwood City, CA • On-site

Other

Posted 9 days ago


Job description

Join us to shape the next frontier of AI-driven robotics!

Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry.

Dyna Robotics was founded by repeat founders Lindon Gao and York Yang, who sold Caper AI for $350 million, and former DeepMind research scientist Jason Ma. The company has raised over $140M, backed by top investors, including CRV and First Round. We're positioned to redefine the landscape of robotic automation. Join us to shape the next frontier of AI-driven robotics.

The Role:

As a Data Annotation Specialist at Dyna Robotics, you will be pivotal in iterating on our AI system by annotating data on various diverse tasks performed by robots. Your will directly influence the performance of our robotic arms, helping them become more accurate and efficient. You will work closely with our engineering and research teams, ensuring data is labeled of the highest quality and meets required standards.

What You'll Do

  • Manually annotate video sequences (boxes/masks/keypoints), track IDs, and label actions & temporal segments
  • Maintain data integrity by applying guidelines and QC checks; resolve ambiguities and fix errors
  • Leverage pre-annotation/autolabeling tools to boost throughput—validate/correct model prelabels and tune auto-tracking/segmentation pipelines

What You'll Bring

  • Associate’s or Bachelor’s degree (or equivalent experience)
  • Strong attention to detail; consistent application of guidelines
  • Ability to follow detailed instructions and work independently with minimal supervision
  • Clear written communication and a collaborative attitude

Bonus Points For

  • Hands-on experience annotating video (boxes/masks/keypoints, action labels, ID tracking)
  • Proficiency with annotation tools; comfort with pre-annotation/autolabel review and correction
  • Familiarity with QA practices (inter-annotator agreement, spot checks, golden sets)
  • Knowledge of common annotation formats (e.g., COCO, YOLO, MOT/KITTI) and basic video concepts (frame rate, codecs)
#J-18808-Ljbffr