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Data Label Jobs in California (NOW HIRING)

In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format ...

Technical Program Manager, Data

San Francisco, CA · On-site

$152K - $196K/yr

Preferred : • Understanding of ML data pipelines and their applications. • Experience working with LLMs. • Familiarity with data labeling for audio technologies, such as speech recognition and ...

Responsibilities : • Annotate visual 3D data (LiDAR/Point Cloud) and 2D camera imagery using bounding boxes, cuboids, polygons, and pixel-level semantic segmentation. • Identify, label, and ...

Data Annotator

Sunnyvale, CA

$136K - $163K/yr

Data Annotator Location: Sunnyvale, CA 94086 (Onsite) Duration: 6 Months with possibility to extend ... Identify, label, and classify objects and environmental details according to strict project ...

New

Proven experience leading data labeling projects. * Experience managing full-time employees or contractors involved in data labeling. * Expertise in writing technical specifications for data ...

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Data Label information

What is the difference between Data Label vs Data Annotator?

AspectData LabelData Annotator
Primary RoleAssigns labels to data for machine learning modelsPerforms detailed annotation of data, including labeling and marking specific features
Skills & CertificationsBasic understanding of data types, labeling toolsMore detailed annotation skills, familiarity with annotation tools
Work EnvironmentData labeling platforms, remote or on-siteAnnotation tools, often similar to labeling platforms
Industry UsageUsed across AI, machine learning, and data science projectsUsed in similar fields, often with more complex annotation tasks

Data Label and Data Annotator roles are closely related, with Data Labeling focusing on assigning simple labels to data, while Data Annotators perform more detailed and complex annotations. Both roles are essential in preparing data for AI and machine learning, often using similar tools and working within the same industry environments.

What are some common challenges faced by Data Labelers, and how can they be addressed?

Data Labelers often face challenges such as handling large volumes of repetitive data, maintaining high accuracy under tight deadlines, and quickly adapting to changing labeling guidelines. To address these challenges, it's important to develop strong attention to detail, use quality control processes like regular peer reviews, and communicate proactively with team leads if guidelines are unclear. Additionally, many teams use specialized annotation tools to streamline workflows and minimize errors, making it helpful to become familiar with these platforms.

What are the key skills and qualifications needed to thrive as a Data Labeler, and why are they important?

To thrive as a Data Labeler, you need strong attention to detail, basic computer literacy, and familiarity with data annotation processes, often supported by a high school diploma or equivalent. Experience with labeling platforms, annotation tools, and sometimes knowledge of data management systems is typically required. Reliability, consistency, and the ability to follow precise instructions are the soft skills that set top performers apart. These skills ensure accurate and high-quality data labeling, which is critical for training effective machine learning models.

What are data labelers?

Data labelers are professionals who annotate or tag data—such as images, text, or audio—to provide context and structure for use in machine learning and artificial intelligence projects. Their work involves identifying and labeling key features in raw data so that algorithms can learn to recognize patterns and make predictions. Data labeling is a crucial step in training supervised learning models, ensuring the accuracy and effectiveness of AI systems.
What cities in California are hiring for Data Label jobs? Cities in California with the most Data Label job openings:
Infographic showing various Data Label job openings in California as of June 2026, with employment types broken down into 1% As Needed, 43% Full Time, 13% Part Time, 5% Temporary, 35% Contract, and 3% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

AI/ML Data Contributor

TSMG

Los Angeles, CA • On-site

Full-time

Posted 20 days ago


Job description

Project Overview
We are currently hiring AI/ML Data Contributors to support a range of active and upcoming projects across the United States. In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing.

Projects may vary in scope and format, offering both remote and in-person opportunities (such as device or VR testing). This is a flexible, task-based role with the opportunity to participate in multiple projects over time.

Responsibilities
  • Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation
  • Participate in remote assignments or attend on-site sessions when required
  • Follow project guidelines and ensure high-quality task completion
  • Provide feedback and input during testing activities
  • Complete tasks within given timelines
Requirements
  • Must be based in the United States
  • Strong attention to detail and ability to follow instructions
  • Basic computer skills and familiarity with digital tools
  • Reliable internet connection and access to a computer or smartphone
  • Availability to participate in task-based work (schedule may vary)
Nice to Have
  • 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 AI/ML projects
  • Exposure to cutting-edge technologies (including device and VR testing)
  • Potential for ongoing project participation
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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