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Ai Labeling Jobs in Texas (NOW HIRING)

DLRdmv

AI Solutions Specialist

Dallas, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

Working knowledge of identity, access control, and data governance controls within Microsoft 365, including sensitivity labels, DLP, and permission scoping, and how these impact AI-driven solutions.

DLRdmv

AI Solutions Specialist

Dallas, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

Working knowledge of identity, access control, and data governance controls within Microsoft 365, including sensitivity labels, DLP, and permission scoping, and how these impact AI-driven solutions.

DLRdmv

AI Solutions Specialist

Dallas, TX · On-site

Working knowledge of identity, access control, and data governance controls within Microsoft 365, including sensitivity labels, DLP, and permission scoping, and how these impact AI-driven solutions.

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Ai Labeling information

What is an AI labeling?

An AI labeling job involves annotating data (such as images, text, audio, or video) to help train machine learning models. This can include tasks like categorizing objects, transcribing speech, or identifying patterns. Accurate labeling is essential for AI systems to learn and improve their performance. The job typically requires attention to detail and an understanding of specific labeling guidelines. It is often performed using specialized software tools provided by AI companies.

What does an AI labeling team member do?

A typical day as an AI Labeling team member involves reviewing large volumes of data—such as images, audio, or text—and accurately tagging or categorizing each piece according to specific guidelines. You’ll often work both independently and in collaboration with a team lead or quality assurance specialist, who reviews and validates your work. Regular communication with teammates and project managers helps clarify any ambiguities and ensures consistency across the project. While the work can be repetitive, it is essential for creating training data that powers AI models, and team members often rotate between different projects for variety and skill development.

What are the key skills and qualifications needed to thrive in AI labeling?

To thrive in an AI Labeling role, you need strong attention to detail, the ability to follow detailed instructions, and basic computer literacy, often requiring a high school diploma or equivalent. Familiarity with data annotation platforms, workflow management tools, and sometimes image or audio editing software is commonly expected. Strong communication, patience, and the ability to quickly learn new guidelines are valuable soft skills in this position. These skills are essential to ensure high-quality, accurate datasets that directly impact the performance of artificial intelligence systems.

How much do AI labelers make?

AI labelers typically earn between $10 and $20 per hour, depending on experience, location, and the complexity of the labeling tasks. Many positions are remote and may require familiarity with data annotation tools and attention to detail.

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

The most popular types of Ai Labeling jobs in Texas are:

What are popular job titles related to Ai Labeling jobs in Texas?

For Ai Labeling jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Ai Labeling jobs in Texas look for?

The top searched job categories for Ai Labeling jobs in Texas are:

What cities in Texas are hiring for Ai Labeling jobs?

Cities in Texas with the most Ai Labeling job openings:

Ai Labeling jobs near you
Infographic showing various Ai Labeling job openings in Texas as of August 2026, with employment types broken down into 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution.

Director, AI & Data Platforms

Teacher Retirement System of Texas

Austin, TX

Full-time

Posted 24 days ago

Job description

The Director of AI & Data Platforms is responsible for leading the enterprise strategy, architecture, engineering, and operations of TRS's artificial intelligence and data platforms. The incumbent will own the shared AI and data foundations that enable scalable, secure, and compliant analytics and AI capabilities across the organization, and ensure that platform capabilities, governance enforcement, and architecture standards are consistently applied to support business-driven data delivery and AI initiatives. This role is a key member of IT leadership and serves as the enterprise point of accountability for AI and data platform execution.

WHAT WILL YOU DO:
Leadership
Builds and leads a high-performing team of platform engineers, architects, AI engineers, and governance specialists.
Develops workforce capabilities in AI and data platform engineering.
Partners with IT leadership on organizational strategy and staffing growth.
Directs department staff, directly and through team leaders, including hiring, directing, monitoring, evaluating, and motivating staff.
Establishes clear career paths and role specialization within AI and data platform domains.
Provides direction, monitors team work loads and work processes, and takes corrective actions as needed to ensure that all operations are covered, and productivity, customer service, and quality goals are met.
Ensures compliance with applicable federal, state, agency, and department policies, procedures, rules, and regulations.
Assesses training needs of team members and arranges for or provides training, coaching, and technical assistance.
Enterprise Enablement & Adoption
Serves as the enabling technology partner for Data Delivery and LOB teams.
Provides "paved roads" for AI and data delivery teams.
Supports developer experience, onboarding, and platform adoption.
Reduces duplication and tool sprawl across the enterprise.
Partners closely with Enterprise Technology Services to achieve enablement goals.
Enterprise AI & Data Platform Strategy, Architecture, and Governance
Defines and execute the strategy for enterprise AI and data platforms (e.g., Fabric, Databricks, Azure AI services).
Oversees platform architecture, engineering, and lifecycle management.
Ensures scalability, reliability, performance, and cost optimization (FinOps).
Establishes and maintains enterprise AI and data platform architecture standards.
Ensures the platform provides the infrastructure and tooling to support AI-ready data patterns (e.g., semantic layers, RAG, data integration patterns).
Drive consistency in interoperability and platform alignment.
Owns platform architecture across the AI and data estate; sets architectural direction for how AI capabilities are built, deployed, and governed on shared infrastructure.
Owns enterprise data governance tooling and enforcement mechanisms, including data catalog (Purview or equivalent), data classification and sensitivity labeling, access controls and policy enforcement.
Ensure governance is implemented through platform capabilities, not manual processes.
Partner with enterprise governance bodies to align policy with technical enforcement.
Platform Engineering, Operations, and Enablement
Leads platform operations including monitoring, incident management, and reliability.
Owns environment strategy (sandbox, development, testing, production).
Establishes DevOps practices including CI/CD, version control, and deployment standards.
Ensures secure, compliant, and auditable platform usage.
Leads development of reusable AI capabilities (e.g., agents, copilots, orchestration frameworks).
Supports AI experimentation, R&D, and transition to production-ready capabilities.
Delivers shared services and patterns that enable downstream delivery teams.
 
Performs related work as assigned. 

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