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Data Annotation Services Jobs in Texas (NOW HIRING)

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Data Scientist II

Austin, TX · On-site

... annotation guidelines and ensuring label quality. • Evaluate and apply the appropriate approach ... service in freight. Founded in 2014, the company is headquartered in Austin, USA, with a team of ...

Sourceability

Principal NLP Scientist

Austin, TX

Experience working with data annotation teams and creating annotation instructions * Experience with .NET / C#, ASP.NET Core, or integration of ML services into enterprise software platforms

TSMG

Robot Teleoperator (AI & Robotics)

Austin, TX

... service environments. This position is ideal for candidates with strong attention to detail ... Support data annotation and quality validation activities * Maintain accurate operational records ...

TSMG

Robot Teleoperator (AI & Robotics)

Austin, TX

... service environments. This position is ideal for candidates with strong attention to detail ... Support data annotation and quality validation activities * Maintain accurate operational records ...

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Data Annotation Services information

What are the key skills and qualifications needed to thrive in data annotation services?

To excel in Data Annotation Services, strong attention to detail, data literacy, and a foundational understanding of data labeling processes are essential, often requiring a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes basic knowledge of scripting or data management systems is typically expected. Strong work ethic, consistency, and effective communication skills help individuals stand out in collaborative, deadline-driven environments. These capabilities ensure high-quality, accurate labeled data, which is critical for training reliable machine learning models.

What is the difference between Data Annotation Services vs Data Labeling Specialists?

AspectData Annotation ServicesData Labeling Specialists
CredentialsTypically no formal credentials required; focus on trainingOften have training in specific tools or industry standards
Work EnvironmentCollaborative, often remote or in-office teamsSimilar, working in teams or independently on labeling tasks
Industry UsageUsed by AI/ML companies for training datasetsEmployed in similar settings, focusing on labeling data for AI models
Search & Comparison IntentUnderstanding services offered for data preparationLooking for roles or tasks related to data labeling

Data Annotation Services encompass the broader process of preparing and annotating data for AI and machine learning projects, often provided by specialized companies. Data Labeling Specialists are individual professionals or team members who perform the actual labeling tasks within these services. While both are closely related, services refer to the overall offering, whereas specialists are the personnel executing the work.

What are some common challenges faced when working in data annotation services, and how can I address them?

In data annotation services, one common challenge is maintaining consistency and accuracy, especially when handling large datasets or ambiguous data points. Clear annotation guidelines and regular communication with team leads help ensure that everyone interprets the data similarly. Additionally, repetitive tasks can lead to fatigue, so it's important to take scheduled breaks and leverage available annotation tools to streamline workflows. Collaborating with peers to discuss edge cases also helps improve overall data quality and fosters a supportive team environment.

What are data annotation services?

Data annotation services involve labeling or tagging data—such as images, text, audio, or video—to make it understandable for machine learning models. These services are essential in training artificial intelligence systems to recognize patterns, objects, or other relevant information in raw data. Companies use data annotation to improve the accuracy and effectiveness of AI applications, such as self-driving cars, chatbots, and image recognition. Professional annotators or specialized platforms often perform these tasks to ensure high-quality, consistent results.
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Data Annotation Services jobs near you
Infographic showing various Data Annotation Services job openings in Texas as of July 2026, with employment types broken down into 2% Locum Tenens, 36% Full Time, 25% Part Time, 1% Contract, 35% Nights, and 1% Summer. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.

Senior Annotation & Quality Manager

Caterpillar

Irving, TX • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago

Caterpillar Inc. rating

7.5

Company rating: 7.5 out of 10

Based on 476 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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