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Data Annotation information

What does a typical workday look like for someone in a Data Annotation role?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

Is data annotation a genuine job?

Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It often requires attention to detail and familiarity with annotation tools, and can be found in various industries like technology and healthcare.

Does data annotation pay well?

Data annotation jobs typically offer entry-level pay that varies depending on the employer, location, and complexity of the tasks. While some positions pay hourly wages comparable to other administrative or clerical roles, experienced annotators working on specialized projects or with advanced tools can earn higher rates. Overall, data annotation is often considered an entry-level position with moderate pay potential.

What is a Data Annotation job?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

How hard is it to get hired by data annotation?

Getting hired for a data annotation role generally requires basic computer skills, attention to detail, and sometimes familiarity with specific tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible, though competition can vary based on the employer and location.

What are the key skills and qualifications needed to thrive in the Data Annotation position, and why are they important?

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

What does a data annotator do?

A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. Attention to detail and knowledge of annotation tools are important for this role.
What are the most commonly searched types of Data Annotation jobs in Irving, TX? The most popular types of Data Annotation jobs in Irving, TX are:
What are popular job titles related to Data Annotation jobs in Irving, TX? For Data Annotation jobs in Irving, TX, the most frequently searched job titles are:
What job categories do people searching Data Annotation jobs in Irving, TX look for? The top searched job categories for Data Annotation jobs in Irving, TX are:
What cities near Irving, TX are hiring for Data Annotation jobs? Cities near Irving, TX with the most Data Annotation job openings:
Data Annotation jobs near you
Infographic showing various Data Annotation job openings in Irving, TX as of July 2026, with employment types broken down into 2% Locum Tenens, 34% Full Time, 25% Part Time, 2% Contract, 36% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution.

Senior Annotation & Quality Manager

Caterpillar

Irving, TX • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 3 days ago

New

Caterpillar Inc. rating

7.5

Company rating: 7.5 out of 10

Based on 475 frontline employees who took The Breakroom Quiz

265th of 487 rated machine equipment manufacturers

Job description

Career Area:
Technology, Digital and Data
Job Description:
Your Work Shapes the World at Caterpillar Inc.
When you join Caterpillar, you're joining a global team who cares not just about the work we do - but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here - we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.
Help Build the Future of Caterpillar.
At Caterpillar, technology always has a purpose, which is to solve our customers' toughest challenges. Through Cat Technology, we are solving problems by building the intelligence layer that connects machines, data, and people to make jobsites safer, more productive, and more sustainable. By combining deep domain expertise in physical systems with software, connectivity, autonomy, and AI, we deliver solutions that work in the real world-on real jobsites, on a global scale.
You'll build and deploy against one of the most unique data foundations-over 1.6 million connected assets generating real-world data daily. These data and platform capabilities are enabling the development of AI models, edge computing architectures, and software systems that scale across fleets, products, and industries. The result will be a new generation of machines that continuously learn, improve, and deliver performance at scale.
Be Part of What's Next in Autonomous Construction Sites
Construction autonomy is one of the most complex challenges in applied AI, and at Caterpillar, advancements in physical AI, simulation, sensing, and edge computing are turning things that once felt impossible-intelligent machines operating in dynamic jobsites-into reality.
Our connected ecosystem brings together massive volumes of high-quality data to create a foundation where engineers like you can build and deploy against.
If this work motivates you, we invite you to join our team. In these roles, you'll work at the intersection of the physical and digital worlds. You'll help design and deliver intelligent systems that enable machines to perceive their environment, make informed decisions, and support safer, more productive operations.
Apply today to build the new era of construction autonomy at Caterpillar.
Role:
We are seeking a Data Annotations & Quality Manager to lead the teams responsible for producing, automating, and validating the datasets that power Physical AI, autonomy, robotics, and machine learning systems.
This leader will oversee three critical functions:
  • Data Annotation - Teams responsible for manual labeling and quality assurance of multimodal sensor data.
  • AI Automation Engineering - Engineers who build and maintain auto-labeling, AI-assisted annotation, and human-in-the-loop systems to improve scalability and efficiency.
  • Data Quality Engineering - Engineers responsible for measuring, monitoring, and enforcing data quality standards across the data lake to ensure datasets remain fit for AI training and production use.

The successful candidate will build and lead a high-performing organization that transforms raw sensor and operational data into trusted, high-quality datasets that enable machine learning, simulation, digital twin, and autonomy initiatives. This role requires a combination of people leadership, operational excellence, data-centric AI expertise, and quality engineering discipline.
What You Will Do
Lead Data Annotation Operations
  • Manage teams responsible for labeling image, video, LiDAR, radar, telemetry, geospatial, and other machine-generated data.
  • Establish scalable annotation processes, standards, and quality controls.
  • Own annotation throughput, quality, cost, and delivery metrics.
  • Drive continuous improvement of annotation workflows, instructions, and quality assurance practices.
  • Partners with AI and software engineering teams align annotation priorities with model development needs.

Lead AI-Powered Annotation Automation
  • Build and lead teams developing auto-labeling, pre-labeling, active learning, and human-in-the-loop annotation solutions.
  • Drive adoption of AI-assisted labeling tools to improve annotation speed and reduce costs.
  • Establish strategies for maximizing automation while maintaining quality and trustworthiness.
  • Define success metrics for automation effectiveness, precision, recall, and reviewer effort reduction.
  • Collaborate with machine learning teams to incorporate model feedback into annotation workflows.

Lead Data Quality Engineering
  • Establish the enterprise data quality strategy for AI training datasets.
  • Define data quality standards, acceptance criteria, and service-level objectives.
  • Implement quality monitoring, anomaly detection, validation rules, and observability capabilities across the data lake.
  • Develop quality scorecards and dashboards that measure dataset health over time.
  • Detect and respond to data degradation, schema drift, annotation drift, missing data, and quality regressions.
  • Ensure training datasets maintain fitness for intended AI use cases.

Deliver Trusted AI Training Data
  • Define data readiness criteria for model training and evaluation.
  • Establish governance for annotation standards, ontologies, labeling guidelines, and dataset versioning.
  • Drive consistency across datasets produced by internal teams and external vendors.
  • Partner with data engineering teams to improve upstream data quality before annotation begins.
  • Partner with machine learning teams to understand model failures and prioritize data improvements.

Build and Develop High-Performing Teams
  • Recruit, develop, and mentor annotation leaders, automation engineers, and data quality engineers.
  • Establish career paths and skills development across all disciplines.
  • Foster a culture focused on quality, innovation, ownership, and continuous improvement.
  • Manage budgets, staffing plans, vendor relationships, and operational priorities.

What You Will Have
Leadership
  • Experience leading technical and operational teams in data, AI, machine learning, analytics, or software engineering environments.
  • Track record of building and scaling high-performing teams.

Data-Centric AI Expertise
  • Strong understanding of how training data impacts machine learning and AI performance.
  • Experience with annotation workflows, ontology management, or dataset development.

Data Quality & Governance
  • Experience establishing data quality standards, monitoring frameworks, and governance processes.
  • Understanding data observability, data validation, and quality measurement techniques.

Software & Automation
  • Experience working with engineering teams building scalable software systems.
  • Familiarity with automation, machine learning workflows, and human-in-the-loop systems.
  • Communication & Influence
  • Ability to communicate effectively with engineering, product, AI, research, and business leaders.
  • Strong stakeholder management and decision-making skills.

Top Candidates Will Have
  • Experience supporting Physical AI, autonomy, robotics, simulation, perception, or digital twin systems.
  • Experience with multimodal data including image, video, LiDAR, radar, GPS, IMU, telemetry, and geospatial data.
  • Experience leading annotation programs involving internal teams, vendors, and AI-assisted labeling systems.
  • Experience building data quality monitoring platforms and observability solutions.
  • Familiarity with active learning, auto-labeling, synthetic data, and human-in-the-loop AI workflows.
  • Experience developing data quality metrics such as completeness, consistency, accuracy, coverage, bias, and drift detection.
  • Experience with cloud-scale data platforms and large data lakes.
  • Experience managing geographically distributed teams.

Additional Details:
  • This position requires the candidate to work full-time at the Irving, Texas office.
  • Domestic relocation assistance is available for this position.
  • Visa sponsorship is available with this position

Summary Pay Range:
$159,120.00 - $258,570.00
Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total compensation at Caterpillar.
Benefits:
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
  • Medical, dental, and vision benefits*
  • Paid time off plan (Vacation, Holidays, Volunteer, etc.)*
  • 401(k) savings plans*
  • Health Savings Account (HSA)*
  • Flexible Spending Accounts (FSAs)*
  • Health Lifestyle Programs*
  • Employee Assistance Program*
  • Voluntary Benefits and Employee Discounts*
  • Career Development*
  • Incentive bonus*
  • Disability benefits
  • Life Insurance
  • Parental leave
  • Adoption benefits
  • Tuition Reimbursement

* These benefits also apply to part-time employees
Posting Dates:
Any offer of employment is conditioned upon the successful completion of a drug screen.
Caterpillar is an Equal Opportunity Employer, Including Veterans and Individuals with Disabilities. Qualified applicants of any age are encouraged to apply.
Not ready to apply? Join our Talent Community.

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