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Senior Video Annotation Jobs (NOW HIRING)

Mill

AI Engineer, Computer Vision

San Bruno, CA · On-site

This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation ... video segmentation) * Experience with foundation models for data annotation * Experience with MLOps ...

Mill

AI Engineer, Computer Vision

San Bruno, CA · On-site

This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation ... video segmentation) * Experience with foundation models for data annotation * Experience with MLOps ...

Mill

AI Engineer, Computer Vision

San Bruno, CA · On-site

This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation ... video segmentation) * Experience with foundation models for data annotation * Experience with MLOps ...

DYNA Robotics Inc

AI Data Strategist

Redwood City, CA · On-site

$148K - $192K/yr

This is a senior individual contributor role that focuses on strategy rather than managing ... Exposure to annotation tooling such as Labelbox, Scale, CVAT, Encord, or Voxel51.

Telus Digital

$132K - $165K/yr

The Opportunity As Senior Account Executive for our Microsoft client account, you will serve as the ... video, image, geo, and 3D data at any scale and complexity. Our data annotation capabilities ...

Uniphore

Sr. AI Engineer

Palo Alto, CA

$122K - $168K/yr

We understand better than anyone how to capture voice, video and text and how to analyze all types ... Specify guidelines and design annotation processes for data; collaborate with data team on data ...

Uniphore

Sr. AI Engineer

Palo Alto, CA · On-site

$122K - $168K/yr

We understand better than anyone how to capture voice, video and text and how to analyze all types ... Specify guidelines and design annotation processes for data; collaborate with data team on data ...

Toptal

$99K - $136K/yr

Job Summary We are building a robust Data Annotation Platform designed to be the backbone of AI ... Whether it's timestamping topics in a video or auditing subtitles, we build the tools that make ...

Oxy

Senior AI - Computer Vision Engineer

Houston, TX

$117K - $154K/yr

We are looking for an experienced and innovative Senior AI - Computer Vision Engineer to join the ... video labeling workflows, using Roboflow or similar annotation tools * Deliver computer vision ...

Voxelcloud

Chief Technology Officer

Los Angeles, CA · On-site

... and video. VoxelCloud's offerings help healthcare providers make better/earlier diagnoses and ... Optimize annotation data quality and label consistency for R&D in a cost-efficient way. o Work with ...

Voxelcloud

Chief Technology Officer

Los Angeles, CA · On-site

... and video. VoxelCloud's offerings help healthcare providers make better/earlier diagnoses and ... Optimize annotation data quality and label consistency for R&D in a cost-efficient way. o Work with ...

Oxy

Senior AI - Computer Vision Engineer

Houston, TX · On-site

$99K - $137K/yr

We are looking for an experienced and innovative Senior AI - Computer Vision Engineer to join the ... video labeling workflows, using Roboflow or similar annotation tools * Deliver computer vision ...

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All JobsSenior Video Annotation Jobs

Senior Video Annotation information

See salary details

$25K

$80.3K

$163.5K

How much do senior video annotation jobs pay per year?

As of Jun 20, 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 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 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 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.

More about Senior Video Annotation jobs
What cities are hiring for Senior Video Annotation jobs? Cities with the most Senior 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 Senior Video Annotation jobs? States with the most job openings for Senior Video Annotation jobs include:
What job categories do people searching Senior Video Annotation jobs look for? The top searched job categories for Senior Video Annotation jobs are:
Senior Video Annotation jobs near you
Infographic showing various Senior Video Annotation job openings in the United States as of June 2026, with employment types broken down into 94% Full Time, 2% Part Time, 3% Contract, and 1% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $80,287 per year, or $38.6 per hour.

AI Engineer, Computer Vision

Mill

San Bruno, CA • On-site

Full-time

Posted 14 days ago

Job description

Mill is a waste prevention technology company reimagining what it means to eliminate waste, starting with food. We build smart systems and infrastructure for homes, businesses, and municipalities that transform food scraps from landfill-bound waste into valuable resources, including chicken feed. Tens of thousands of Mill's residential food recyclers are already helping households divert millions of pounds of food scraps every year, paving the way for our upcoming launch of Mill Commercial-the industry's first end-to-end solution for managing, understanding, and preventing food waste in commercial environments (e.g. grocery, restaurants, food services). At Mill, we are passionate about building easy-to-use, beautifully designed technologies that keep food in the food system and out of landfills.
About the Role
We're hiring an AI Engineer to work on the AI core of Mill Commercial - the computer vision and agentic systems that turn a stream of food waste into operational intelligence for commercial kitchens. Mill Commercial integrates a camera and onboard compute directly into our high-capacity food recycler; models running on the edge identify, classify, and quantify food scraps at the point of generation, and our vision pipeline turns that signal into procurement and operational guidance for large food service operators.
You'll join a small AI team, building the data and training pipeline that produces our edge CV models, designing the cloud-side evaluation that tells us whether those models are good enough to ship, and helping build the agentic, LLM-driven product features that turn raw waste data into customer-facing insights and recommendations. This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation model, prompting a VLM, and wiring an agent into a product feature.
What You'll Do
  • Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling (including foundation-model-assisted labeling), training, evaluation, and retraining cycles.
  • Train and evaluate segmentation, classification, and mass-estimation models for the Mill Commercial camera pipeline - from prompting foundation models to fine-tuning ConvNets and VLMs.
  • Build the cloud-side evaluation harness that tells us how our shipped edge models are actually performing in the field - automated, reproducible, and aligned to product accuracy targets across food types, kitchen environments, and deployment configurations.
  • Own MLOps: reproducible training, experiment tracking, model versioning, and automated evaluation against product-defined accuracy targets.
  • Export and validate models for deployment to edge devices, working closely with the edge team on optimization, quantization, and integration.
  • Help design and build the LLM- and agent-powered product features that consume waste characterization data and turn it into customer-facing recommendations - purchasing suggestions, anomaly explanations, operational nudges. Define how agents call tools, ground in customer data, and stay reliable in production.
  • Analyze failure cases systematically - unfamiliar food classes, novel kitchen environments, challenging lighting and clutter conditions - and drive the data and modeling decisions that close accuracy gaps.
What We're Looking For
  • Strong fundamentals in computer vision and deep learning - segmentation, detection, classification, tracking. You understand the architectures well enough to make informed choices.
  • Fluency with modern ML approaches - VLMs, LLMs, foundation models, and agentic systems - alongside classical deep learning. You know when to fine-tune a ConvNet, when to prompt a VLM, and when to wire up an agent, and you understand the practical realities of putting any of them into a product.
  • Experience building ML training pipelines and data annotation systems at scale.
  • Experience evaluating ML models rigorously - designing metrics, building the eval harness, and using results to drive product decisions rather than just publish a number.
  • Proficiency with cloud ML infrastructure (AWS or equivalent) - you've managed training jobs, data pipelines, and experiment workflows in production.
  • Familiarity with cloud-to-edge model deployment.
  • Clear, direct communication - you can explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow.
  • Genuine interest in applying AI to food waste reduction and sustainability. This is a mission-driven product and we want people who care about the mission.

Software skills: Python, PyTorch, OpenCV. Strong familiarity with MLOps on AWS infrastructure. Experience with LLM and agent frameworks. Google Cloud / Gemini experience is a plus.
Nice to Have
  • Experience with video understanding (temporal consistency, tracking, video segmentation)
  • Experience with foundation models for data annotation
  • Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, or equivalents)
  • Experience shipping LLM- or agent-powered features in a consumer or B2B product
  • Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems

The estimated base salary range for this position is $240 to $280k, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.

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