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Annotation Qa Jobs in California (NOW HIRING)

XDOF

Senior Perception Engineer

San Mateo, CA · On-site

$180 - $240/hr

Contribute to the design of automated annotation pipelines that convert sensor data into structured training labels * Build Auto QA modules to filter low-quality data including anomalous frames ...

xdof, Inc.

Senior Perception Engineer

San Mateo, CA · On-site

$150 - $210/hr

Contribute to the design of automated annotation pipelines that convert sensor data into structured training labels * Build Auto QA modules to filter low-quality data including anomalous frames ...

Welocalize

Lead Gen AI Analyst

Santa Clara, CA · On-site +1

$50/hr

Experience managing QA operations, calibration sessions, audits, and quality improvement initiatives. * Familiarity with annotation tools and AI data workflows. * Experience working with Product ...

Abaka AI

Expert Operations

Mountain View, CA · On-site

$80K - $130K/yr

Bachelor's degree or equivalent practical experience. * 1+ years of experience in AI data annotation, quality assurance, reviewer operations, project operations, talent operations, recruiting, or ...

Abaka AI

Expert Operations

Mountain View, CA · On-site

$80K - $130K/yr

Bachelor's degree or equivalent practical experience. * 1+ years of experience in AI data annotation, quality assurance, reviewer operations, project operations, talent operations, recruiting, or ...

Welo Data

Lead Gen AI Analyst

Santa Clara, CA · On-site +1

$50/hr

Experience managing QA operations, calibration sessions, audits, and quality improvement initiatives. * Familiarity with annotation tools and AI data workflows. * Experience working with Product ...

Sieve

Data Operations Lead

San Francisco, CA · On-site

... annotation, curation, and quality review • Build and improve QA processes to ensure data output meets the standards required by frontier AI labs • Own product ops for the data platform. Work with ...

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All JobsAnnotation Qa JobsAnnotation Qa Jobs in California

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 are popular job titles related to Annotation Qa jobs in California?

For Annotation Qa jobs in California, the most frequently searched job titles are:

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:

Annotation Qa jobs near you
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

Menlo Ventures

San Mateo, CA • On-site

$90 - $130/hr

Other

Posted 28 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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