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Full Time Machine Learning Data Annotation Jobs in Texas

Dow

Machine Learning Engineer

Houston, TX · On-site

As a Machine Learning Engineer in our Data & Analytics Platforms team specializing in data and analytics solutions , you will work with a cross-functional team whose objective is to deliver solutions ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Data Science / Engineering Employment Type: Full-time About the Role: We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $138K/yr

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. They are seeking an experienced Senior Machine Learning Engineer to design, develop ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $138K/yr

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. They are seeking an experienced Senior Machine Learning Engineer to design, develop ...

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All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in Texas

Full Time Machine Learning Data Annotation information

What are the key skills and qualifications needed to thrive as a full time machine learning data annotation specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What is a full time machine learning data annotation job?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Texas? The most popular types of Machine Learning Data Annotation jobs in Texas are:
What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Texas? For Full Time Machine Learning Data Annotation jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Texas look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Texas are:
What cities in Texas are hiring for Full Time Machine Learning Data Annotation jobs? Cities in Texas with the most Full Time Machine Learning Data Annotation job openings:
Full Time Machine Learning Data Annotation jobs near you
Infographic showing various Full Time Machine Learning Data Annotation job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Annotation & Quality Manager

Caterpillar

Irving, TX • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Caterpillar Inc. rating

7.5

Company rating: 7.5 out of 10

Based on 477 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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