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Internship Ai Data Labeling Jobs in California (NOW HIRING)

Welocalize

Lead Gen AI Analyst

Santa Clara, CA · On-site +1

$50/hr

What We're Looking For * 5+ years of experience in AI data operations, data annotation, content quality, Trust & Safety, data labeling, or similar production environments. * 3+ years of experience ...

New

UJET

AI Data Engineer

Los Angeles, CA · On-site

$123K - $148K/yr

We are hiring an AI Data Engineer to own the seam between "customer says yes" and "data is flowing ... internship, contract, or full-time all qualify * Daily fluency with a standard engineering workflow ...

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Internship Ai Data Labeling information

What is an AI data labeling internship?

An AI Data Labeling Internship is a temporary position where interns assist in preparing datasets for machine learning models by accurately annotating, categorizing, or tagging data such as images, text, or audio. Interns learn about the fundamentals of artificial intelligence and the importance of high-quality labeled data in training algorithms. This role is ideal for students or recent graduates interested in AI, data science, or related fields, and provides hands-on experience with data preparation and quality assurance processes.

What are the key skills and qualifications needed to thrive as an AI data labeling intern?

To thrive as an AI Data Labeling Intern, you need attention to detail, basic data analysis skills, and familiarity with data annotation concepts, often supported by a background in computer science or related fields. Experience using annotation platforms, spreadsheets, and sometimes specific labeling software is common, though formal certifications are not usually required. Strong communication, time management, and the ability to follow detailed guidelines set high performers apart in this role. These skills ensure accurate, high-quality data sets that are essential for training reliable AI models.

What are some common challenges faced during an AI data labeling internship, and how can I overcome them?

As an AI Data Labeling intern, you may encounter challenges such as maintaining high accuracy while labeling large volumes of data, understanding complex labeling guidelines, and managing repetitive tasks without losing focus. To overcome these, it's helpful to regularly review the instructions, seek feedback from your team lead, and use productivity techniques to stay engaged. Collaborating with other interns and attending team meetings can also provide valuable insights and help you address uncertainties quickly.

What is the difference between Internship Ai Data Labeling vs Data Annotation Specialist?

AspectInternship Ai Data LabelingData Annotation Specialist
Required CredentialsHigh school diploma or equivalent; some technical skillsHigh school diploma or higher; technical skills often preferred
Work EnvironmentEntry-level, training-focused, often remote or in-officeProfessional setting, may be remote or on-site, more independent
Employer & Industry UsageTech companies, AI startups, research projectsAI companies, data service providers, tech firms
Search & Comparison IntentUnderstanding entry-level roles in AI data labelingClarifying professional data annotation roles

Internship Ai Data Labeling typically refers to entry-level, training-focused positions aimed at gaining experience in labeling data for AI models. Data Annotation Specialist is a more experienced, professional role involving detailed data labeling tasks. Both roles are essential in AI development, but internships are designed for beginners, while specialists have more responsibility and expertise.

What are the most commonly searched types of Ai Data Labeling jobs in California?

The most popular types of Ai Data Labeling jobs in California are:

What are popular job titles related to Internship Ai Data Labeling jobs in California?

For Internship Ai Data Labeling jobs in California, the most frequently searched job titles are:

What job categories do people searching Internship Ai Data Labeling jobs in California look for?

The top searched job categories for Internship Ai Data Labeling jobs in California are:

What cities in California are hiring for Internship Ai Data Labeling jobs?

Cities in California with the most Internship Ai Data Labeling job openings:

Internship Ai Data Labeling jobs near you
Infographic showing various Internship Ai Data Labeling job openings in California as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Lead Gen AI Analyst

Welocalize

Santa Clara, CA • On-site, Remote

$50/hr

Full-time

Posted yesterday

New

Welocalize rating

6.5

Company rating: 6.5 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

334th of 494 rated business services

Job description

We're looking for an experienced Lead Generative AI Analyst to join our team. This leadership role is open to both hybrid and remote candidates, with a preference for those who can work in a hybrid capacity from our Santa Clara, CA office. We're looking for someone who enjoys coordinating people, solving operational challenges, and delivering high-quality results in a fast-paced AI environment.

Rather than focusing on individual annotation work, you'll lead the day-to-day execution of AI data operations, partnering with annotators, quality teams, and cross-functional stakeholders to ensure projects are delivered efficiently, consistently, and at the highest quality standards.

If you have a strong background in project management, operations, or leading production teams—and you're excited about working on cutting-edge Generative AI initiatives—this role is for you.

What You'll Do

  • Lead the day-to-day operations of a team of multimodal AI annotators and reviewers.
  • Provide people leadership, including coaching, performance management, workload planning, and team development.
  • Drive project execution by balancing quality, delivery timelines, and operational priorities.
  • Own client-facing meetings, providing project updates, discussing risks, action plans, and delivery progress.
  • Serve as the primary point of contact for workflow questions, issue resolution, and project escalations.
  • Monitor team performance, quality metrics, productivity, and project health, identifying opportunities for continuous improvement.
  • Develop and refine annotation guidelines, documentation, and quality standards as projects evolve.
  • Coordinate calibration sessions and quality reviews to ensure consistent decision-making across the team.
  • Partner with cross-functional stakeholders, including Quality, Operations, Product, and Engineering teams, to resolve complex cases and improve workflows.
  • Support onboarding, coaching, and ongoing development of new team members.
  • Identify operational risks early and proactively drive solutions to keep projects on track.
  • Prepare regular reports on project status, quality trends, and key operational metrics.

What We're Looking For

  • 5+ years of experience in AI data operations, data annotation, content quality, Trust & Safety, data labeling, or similar production environments.
  • 3+ years of experience leading teams and managing projects, with ownership of delivery, quality, and operational outcomes.
  • Demonstrated experience managing personnel, priorities, and multiple workstreams simultaneously.
  • Experience leading client meetings and communicating project status to internal and external stakeholders.
  • Strong stakeholder management and communication skills.
  • Excellent written and verbal English.
  • Exceptional organizational skills with the ability to prioritize in a fast-moving environment.
  • Experience creating documentation, operational processes, or quality guidelines.
  • Comfortable making decisions in ambiguous situations while maintaining consistency and accuracy.

Preferred Qualifications

  • Experience leading AI, machine learning, or multimodal annotation programs.
  • Experience managing QA operations, calibration sessions, audits, and quality improvement initiatives.
  • Familiarity with annotation tools and AI data workflows.
  • Experience working with Product, Engineering, Research, or Vendor Management teams.
  • Knowledge of reporting, dashboards, workflow optimization, or process improvement.
  • Basic Python, SQL, or data analysis skills are a plus.

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