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Annotation Qa Jobs in California (NOW HIRING)

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning. * Implement monitoring and reliability frameworks, including ...

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $150K/yr

... annotation, QA workflows, dataset management, augmentation, and versioning. • Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated ...

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

ML Data QA Lead, MLO

Cupertino, CA · On-site

$121 - $249/hr

Description The Machine Learning Data Ops QA team ensures that Research and Development teams ... We support our data collection, annotation and synthesis partners with defining quality standards ...

We support our data collection, annotation and synthesis partners with defining quality standards ... You will also build and extend the team's QA tooling, including review interfaces, analysis ...

Quality Control Lead

San Francisco, CA · On-site

$150K - $275K/yr

Experience in AI training data, RLHF, or data-annotation quality assurance * Background reviewing datasets across multiple domains (code, reasoning, knowledge work) * Experience designing rubrics ...

New

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning. * Implement monitoring and reliability frameworks, including ...

Showing results 21-40

Annotation Qa information

What is the difference between Annotation Qa vs Data Labeler?

AspectAnnotation QaData Labeler
Required CredentialsBasic technical skills, sometimes certifications in data annotation toolsBasic computer skills, sometimes certifications in labeling software
Work EnvironmentOffice or remote, collaborative with annotation teamsRemote or on-site, focused on labeling tasks
Industry UsageUsed in AI, machine learning, and data annotation companiesCommon in AI, machine learning, and data preparation sectors
Search & Comparison IntentOften compared for quality assurance roles in data annotationCompared for entry-level data preparation roles

Annotation Qa and Data Labeler roles are closely related in the data annotation industry. Annotation Qa focuses on quality assurance, reviewing and verifying labeled data, while Data Labelers perform the initial labeling tasks. Both require similar technical skills and work environments, but Annotation Qa emphasizes quality control processes. Understanding these differences helps employers and job seekers identify the right role based on skills and career goals.

What job categories do people searching Annotation Qa jobs in California look for?

The top searched job categories for Annotation Qa jobs in California are:

What cities in California are hiring for Annotation Qa jobs?

Cities in California with the most Annotation Qa job openings:

Infographic showing various Annotation Qa job openings in California as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 6% Part Time, 4% Contract, and 1% Nights. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.

Computer Vision AI & ML Engineer

Skild AI

San Mateo, CA • On-site

$127K - $149K/yr

Full-time

Re-posted 2 days ago


Job description

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.