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Ai Annotation Jobs in California (NOW HIRING)

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

Company Overview At Skild AI, we are building the world's first general purpose robotic ... Design labeling strategies and tooling for automated annotation, QA workflows, dataset management ...

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

Position Overview We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy ... Design labeling strategies and tooling for automated annotation, QA workflows, dataset management ...

Dyna Robotics

AI Data Strategist

Redwood City, CA · On-site

$148K - $192K/yr

Required : • 4-8+ years of experience working in AI/ML, robotics, autonomy, or data-centric systems roles. • Proven experience defining data quality standards, evaluation frameworks, annotation ...

Genesis10

AI Linguist - Hybrid

Mountain View, CA · On-site

$38.61 - $45.48/hr

Design and execute on solutions for AI quality, evaluation, and annotation * Leverage Generative AI prompting, human input, hybrid approaches in the development of scalable and high-quality human ...

Genesis10

AI Linguist - Hybrid

Mountain View, CA · On-site

$38.61 - $45.48/hr

Design and execute on solutions for AI quality, evaluation, and annotation Leverage Generative AI prompting, human input, hybrid approaches in the development of scalable and high-quality human ...

SpaceXAI

AI Tutor - Chinese

Palo Alto, CA · On-site

Work with technical staff to improve annotation tools for efficient audio workflows. BASIC ... Commitment to developing AI that masters sophisticated multilingual audio capabilities. PREFERRED ...

SpaceXAI

AI Tutor - Arabic

Palo Alto, CA

Work with technical staff to improve annotation tools for efficient audio workflows. BASIC ... Commitment to developing AI that masters sophisticated multilingual audio capabilities. PREFERRED ...

Dyna Robotics

AI Data Strategist

Redwood City, CA · On-site

$148K - $192K/yr

Required : • 4-8+ years of experience working in AI/ML, robotics, autonomy, or data-centric systems roles. • Proven experience defining data quality standards, evaluation frameworks, annotation ...

SpaceXAI

AI Tutor - Danish

Palo Alto, CA · On-site

Work with technical staff to improve annotation tools for efficient audio workflows. BASIC ... Commitment to developing AI that masters sophisticated multilingual audio capabilities. PREFERRED ...

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Ai Annotation information

What is an AI annotation?

An AI Annotation job involves labeling, tagging, or annotating data, such as images, text, or audio, to train machine learning models. Annotators help improve AI accuracy by providing high-quality, structured data that algorithms use to learn patterns. Tasks may include identifying objects in images, transcribing speech, or classifying text-based content. This job is essential for developing AI applications like self-driving cars, chatbots, and image recognition systems.

What does an AI annotation specialist do?

As an AI Annotation specialist, your typical day will involve accurately labeling, categorizing, or tagging large volumes of images, text, audio, or video data to train AI models according to project guidelines. You may work independently or as part of a team, using specialized annotation platforms and regularly reviewing your work to ensure quality and consistency. Collaboration with data scientists or project managers may be required to clarify ambiguous cases or update labeling criteria. You can expect periodic feedback and performance reviews to help refine your skills and ensure the data meets the project’s standards, making attention to detail and adaptability essential for success.

What are the key skills and qualifications needed to thrive in the AI annotation position?

To thrive as an AI Annotation professional, you need keen attention to detail, strong analytical skills, and a basic understanding of machine learning concepts, often supported by a high school diploma or relevant technical training. Familiarity with data labeling tools, annotation platforms such as Labelbox or Supervisely, and basic spreadsheet or database management is commonly required. Strong communication, time management, and the ability to maintain focus during repetitive tasks are standout soft skills. These abilities are crucial for producing high-quality, consistent data that supports the effective development and accuracy of AI models.

What are the most commonly searched types of Ai Annotation jobs in California? The most popular types of Ai Annotation jobs in California are:
What are popular job titles related to Ai Annotation jobs in California? For Ai Annotation jobs in California, the most frequently searched job titles are:
What job categories do people searching Ai Annotation jobs in California look for? The top searched job categories for Ai Annotation jobs in California are:
What cities in California are hiring for Ai Annotation jobs? Cities in California with the most Ai Annotation job openings:
Ai Annotation jobs near you
Infographic showing various Ai Annotation job openings in California as of August 2026, with employment types broken down into 67% Full Time, 28% Part Time, 2% Temporary, and 3% Contract. Highlights an 67% Physical, 3% Hybrid, and 30% Remote job distribution.

Computer Vision AI & ML Engineer

Skild AI

San Mateo, CA • On-site

$127K - $149K/yr

Full-time

Re-posted 10 days ago

Job description

Company Overview
At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.
Position Overview
We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy advanced perception systems for real-world robotics and automation. You will work across the full machine learning lifecycle-model development, data strategy, evaluation, and production integration-to deliver robust, high-performance vision capabilities. This role combines applied research with hands-on engineering and offers the opportunity to influence both architecture and roadmap decisions.
Responsibilities
  • Develop and optimize deep learning models for depth estimation, object detection, segmentation, tracking, and 3D scene understanding using multi-modal sensor data.
  • Build scalable pipelines for data processing, training, evaluation, and deployment into real-world and real-time systems.
  • Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning.
  • Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated performance reporting.
  • Conduct proof-of-concept experiments to evaluate new algorithms and perception techniques; translate research insights into practical prototypes.
  • Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance.
Preferred Qualifications
  • Strong experience with deep learning frameworks (PyTorch, TensorFlow, or JAX).
  • Background in computer vision tasks such as detection, depth estimation, segmentation, tracking, or 3D scene understanding.
  • Proficiency in Python; familiarity with C++ is a plus.
  • Experience building training pipelines, evaluation frameworks, and ML deployment workflows.
  • Knowledge of 3D geometry, sensor processing, or multi-sensor fusion (RGB-D, LiDAR, stereo).
  • Experience with data annotation tools, dataset management, and augmentation techniques.
  • Familiarity with robotics, simulation environments (Isaac Sim, Gazebo, Blender), or real-time systems.
  • Understanding of uncertainty modeling, reliability engineering, or ML monitoring/MLOps practices.

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