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Senior Video Annotation Jobs (NOW HIRING)

Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... video, or simulation. * Familiarity with 3D geometry, camera models, coordinate frames, and ...

Experience with large‑scale image/video datasets. * Preferred: Familiarity with MLOps tools (e.g., DVC), annotation platforms, and compliance standards. * Experience in computer vision, multimedia ...

Senior AI Data Science Engineer

Sunnyvale, CA · On-site

$124K - $169K/yr

Drive annotation workflows, including label taxonomy, tooling selection, and quality control, in ... Track record of designing and building data pipelines for multi-modal datasets (video, time-series ...

## Senior Developer Relations Manager - World Models RoboticsApplylocations: US, CA, Santa Claratime ... Working knowledge of Physical AI data workflows: large-scale video curation, captioning/annotation ...

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Senior Video Annotation information

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$25K

$80.3K

$163.5K

How much do senior video annotation jobs pay per year?

As of Sep 12, 2026, the average yearly pay for senior video annotation in the United States is $80,287.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,500.00 and $103,000.00 per year, depending on experience, location, and employer.

What is a senior video annotation?

A Senior Video Annotation specialist is a professional responsible for labeling, tagging, and categorizing objects or actions within video data, often for use in machine learning and artificial intelligence projects. They oversee and guide annotation teams, ensure high-quality data labeling, and help develop guidelines and best practices. Their expertise is crucial for training accurate computer vision models, as they provide the ground truth data that algorithms learn from.

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

To excel as a Senior Video Annotation Specialist, you need advanced skills in data labeling, attention to detail, and experience with video annotation tools, often supported by a degree in computer science or a related field. Familiarity with annotation platforms like CVAT, Labelbox, or VGG Image Annotator, and understanding of basic machine learning concepts, are typically required. Strong organizational skills, problem-solving abilities, and effective communication help ensure accuracy and seamless collaboration with data science teams. These competencies are vital for producing high-quality annotated datasets that drive the performance of computer vision models.

What are some common challenges faced by senior video annotation professionals, and how can they be addressed?

Senior Video Annotation professionals often encounter challenges such as maintaining consistency in labeling complex visual data, meeting tight project deadlines, and managing large volumes of video content. To address these issues, it's important to establish clear annotation guidelines, utilize efficient annotation tools, and foster open communication within the annotation team. Regular training and quality assurance checks can also help ensure high accuracy and efficiency, positioning team members for leadership and quality control roles as they advance.

What is the difference between Senior Video Annotation vs Video Labeler?

AspectSenior Video AnnotationVideo Labeler
Required CredentialsTypically requires experience in annotation tools, basic understanding of video content, and sometimes a degree in related fieldsUsually requires familiarity with labeling software and basic video content understanding, but less experience needed
Work EnvironmentOften part of a team working on complex projects, possibly remote or in-officeTypically focused on individual tasks, often remote, with repetitive labeling work
Employer & Industry UsageUsed in AI/ML companies, autonomous vehicle development, and tech firmsCommon in data annotation companies, AI startups, and research labs

Senior Video Annotation roles involve more complex tasks, oversight, and experience, while Video Labelers focus on basic labeling tasks. The senior role often requires a deeper understanding of video content and annotation tools, making it suitable for those with more experience. Both roles are essential in AI data preparation but differ in scope and responsibility.

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Infographic showing various Senior Video Annotation job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 75% Physical, 2% Hybrid, and 23% Remote job distribution, with an average salary of $80,287 per year, or $38.6 per hour.

Sr. Research Data Scientist

San Diego, CA • On-site

Full-time

Dental, Vision, Life, Retirement, PTO

Re-posted 5 days ago


Job description

Aicadium is looking for a Senior Research Data Scientist to push the boundaries of computer vision and machine learning in real-world industrial settings. This is a hybrid role based in San Diego, where our U.S. headquarters is located.
You'll drive applied R&D across the full research lifecycle from conducting original research and prototyping state-of-the-art models to refining and validating them using messy, real-world visual data.


The ideal candidate brings deep technical expertise in modern computer vision, strong research instincts, and the communication skills to work effectively across research, engineering, and product teams.

Responsibilities:

  • Research, prototype, and develop state-of-the-art computer vision and deep learning models spanning detection, segmentation, tracking, and vision-language tasks.
  • Translate recent research into working implementations, reproducing baselines, running ablations, and quantifying practical tradeoffs.
  • Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation, synthetic data generation, and quality analysis.
  • Collaborate closely with engineering and product teams to move models from prototype to production, including optimization for edge or latency-constrained environments.
  • Communicate methods, results, and tradeoffs clearly to both technical and non-technical stakeholders.

Skills & Requirements:

  • Master's or PhD in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related field, or a Bachelor's with equivalent research or industry experience.
  • Strong foundation in modern computer vision and deep learning: image classification, object detection, segmentation, and tracking across CNNs, Vision Transformers, vision-language models, and foundation models.
  • Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation learning, optimization, loss design, and rigorous model training and evaluation.
  • Demonstrated ability to read, implement, and build on recent research papers, turning literature into working prototypes.
  • Hands-on experience with real-world visual data: dataset curation, annotation, augmentation, synthetic data, data-quality analysis, and low-data or noisy-data settings.
  • Strong applied mathematics (linear algebra, probability, statistics, optimization) and proficiency in Python with PyTorch or equivalent deep learning framework.
  • Strong collaboration and communication skills - able to work across research, engineering, and product, and present technical work clearly to mixed audiences.


Nice to Have:

  • Proven ability to reproduce, evaluate, and extend recent research, including careful baseline comparisons, ablation studies, and tradeoff analysis.
  • Experience in robotic vision, vision-language-action systems, world models, imitation learning, reinforcement learning, or generative models for image, video, or simulation.
  • Familiarity with 3D geometry, camera models, coordinate frames, and calibration.
  • Publications at top-tier venues, open-source contributions, research internships, or substantial project experience in computer vision, robotics, or embodied AI.
  • Hands-on experience deploying or optimizing models for production or edge environments - model compression, quantization, ONNX, TensorRT, or latency optimization.


About Us:

Aicadium is a global technology company delivering AI-powered industrial computer vision products into the hands of enterprises. With offices in Singapore and San Diego, California, and an international team of data scientists, engineers, and business strategists, Aicadium is operationalizing AI within organizations where advanced machine learning innovations were previously out of reach.


Team

Join a growing team of data scientists, machine learning, and software engineers in an agile development environment. Work together with some of the best in the field to tackle challenging projects and operationalize the solutions you develop across a variety of industries and use cases.

Culture

We work in a casual and collaborative startup environment. Every member of the team plays a key role in shaping the solutions we develop and creating positive business value for the companies we work with. We are building a hub of the best talent in San Diego, CA.


Benefits

Aicadium has a great benefits package to come with your salary. Benefits include PTO, Heath insurance, Vision and Dental Insurance, Life and AD&D, 401k with matching, and more!