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Google Artificial Intelligence Data Annotation information

What are the key skills and qualifications needed to thrive as a Google Artificial Intelligence Data Annotation Specialist, and why are they important?

To thrive as a Google Artificial Intelligence Data Annotation Specialist, you need strong attention to detail, data literacy, and the ability to follow complex guidelines, often supported by a bachelor's degree or relevant work experience. Familiarity with data labeling tools, basic coding skills (such as Python), and experience using annotation platforms like Labelbox or internal Google tools are typically required. Standout candidates possess strong analytical thinking, adaptability, and effective communication to ensure clarity and accuracy in large datasets. These skills are crucial for producing high-quality annotated data, which directly impacts the effectiveness and accuracy of AI and machine learning models.

How to become an AI data annotator?

To become an AI data annotator, you typically need strong attention to detail, good communication skills, and basic computer literacy. Familiarity with annotation tools and understanding of data labeling guidelines are also important; some roles may require prior experience or training in specific domains like images, videos, or text. Many positions are entry-level and offer flexible schedules, making them accessible to a wide range of applicants.

What is the difference between Google Artificial Intelligence Data Annotation vs Data Labeler?

AspectGoogle Artificial Intelligence Data AnnotationData Labeler
CredentialsTypically no formal degree, but familiarity with AI tools helpfulOften no formal credentials required
Work EnvironmentRemote or on-site, working with AI datasetsPrimarily remote or on-site data labeling tasks
Industry UsageUsed in AI and machine learning projects for training modelsUsed across industries for data preparation
Job FocusAnnotating data for AI algorithms, ensuring accuracyLabeling data such as images, text, or audio

Google Artificial Intelligence Data Annotation involves preparing datasets specifically for AI model training, often requiring understanding of AI workflows. Data Labelers focus on tagging data accurately across various formats. While both roles involve data handling, AI Data Annotation emphasizes AI-specific tasks, whereas Data Labelers perform broader data tagging tasks.

What is Google Artificial Intelligence Data Annotation?

Google Artificial Intelligence Data Annotation refers to the process of labeling or tagging data—such as images, videos, audio, or text—so that it can be used to train machine learning models. Data annotators review content and apply relevant tags or categories, helping AI systems better understand and process real-world data. This work is crucial for improving the accuracy and reliability of AI products, such as search engines, voice assistants, and image recognition tools. Annotation can be manual or assisted by software, depending on the complexity and type of data involved.

Which 3 jobs will survive AI?

For a Google Artificial Intelligence Data Annotation role, jobs that require complex human judgment, creativity, and emotional intelligence are more likely to survive AI automation. These include roles such as AI ethicists, creative professionals, and specialized technical experts. Skills in critical thinking, problem-solving, and domain-specific knowledge will remain valuable in the evolving AI landscape.

Are data annotations still hiring?

Data annotation roles for artificial intelligence, including positions like Google AI Data Annotation, are currently in demand as companies continue to develop machine learning models. These jobs often require attention to detail, familiarity with annotation tools, and sometimes basic knowledge of AI concepts, with opportunities available in both full-time and freelance capacities.

What are some common challenges faced by Artificial Intelligence Data Annotators at Google, and how can they be addressed?

Artificial Intelligence Data Annotators at Google often face challenges such as maintaining accuracy and consistency when labeling large volumes of complex data, adapting to evolving project guidelines, and meeting tight deadlines. To address these challenges, annotators benefit from thorough onboarding, ongoing training sessions, and access to clear documentation. Collaborating closely with team leads and machine learning engineers helps resolve ambiguities and ensures alignment with project objectives, creating an environment where questions can be addressed promptly and quality standards are upheld.

How much do AI data annotators make?

AI data annotators typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Many positions are freelance or part-time, requiring attention to detail and familiarity with annotation tools. Salaries can vary based on the employer and project scope.
Infographic showing various Google Artificial Intelligence Data Annotation job openings in the United States as of June 2026, with employment types broken down into 8% Full Time, 67% Part Time, and 25% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.
Data Domain Architect Lead

Data Domain Architect Lead

JP Morgan Chase

Wilmington, DE

Full-time

Medical, Retirement

Posted 18 days ago

JPMorgan Chase & Co. rating

8.1

Company rating: 8.1 out of 10

Based on 470 frontline employees who took The Breakroom Quiz

46th of 141 rated banks

Job description

Machine Learning and Artificial Intelligence play a critical role in transforming Consumer and Community Banking Operations. The ability to utilize data in meaningful ways allows us to develop solutions which both our customers and employees can benefit from. Customers expect tailored servicing and Chase is looking to deliver personalization to meet their needs. This is powered by high-quality annotated data and detailed annotation schemes that are the backbone of impactful  Artificial Intelligence/Machine Learning ( AI/ML)L algorithms and applications.

As a Data Domain Architect Lead within the Data  Annotation team , you will use your domain expertise and people-leading experience to partner your team closely with teams in Data Science, Analytics, and Engineering to develop machine learning solutions. This will involve the collection, curation, annotation, enrichment, and validation of data and the development of taxonomies and other linguistic resources to help train machine learning models, drive insight, analysis, and possible content creation.

Job responsibilities

  • Manage and coach a team of Machine Learning Data Domain analysts to support data annotation and label data/content using annotation tools and analysis

  • Partner with leads in Data Science, Engineering, and Analytics to develop strategies to optimize training data for machine learning models
  • Lead efforts to identify patterns and trends in conversational data through Natural Language Processing and/or other computational linguistic approaches
  • Collaborate with stakeholders on evaluating the quality of machine learning classification and other output
  • Actively contribute to the team's continuous learning mindset by bringing in new ideas and perspectives that stretch the thinking of the group

Required qualifications, capabilities, and skills

  • 6+ years of related experience in development of machine learning solutions
  • Familiar with industry annotation and labeling methods
  • Experience with various data modeling techniques and tools
  • Familiar with Finance and Banking products
  • Broad expertise in data technologies; i.e., data warehousing, data processing, data quality concepts, Business Intelligence tools and analytical tools, unstructured data, machine learning
  • Excellent analytical and problem-solving skills and the ability to pay close attention to detail
  • Experience using Python in working with and analyzing large real-world datasets
  • Working knowledge of information and data retrieval
  • Working knowledge of machine learning and artificial intelligence paradigms and libraries
  • Familiar with  Large Language Models (LLMs) and prompt engineering

Preferred qualifications, capabilities, and skills

  • Masters or PhD in a related field, or Bachelors 
  • Technical understanding of common relational database systems; i.e., Teradata and Oracle
  • Excellent command of the Structured Query Language (SQL)
  • Knowledge of SAS or Scala, and Python languages
  • Knowledge of Advanced Statistics
  • Advanced analytical thinking and problem-solving skills
  • Strong interpersonal & communication skills

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

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