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Full Time Data Annotation Tech Jobs in Austin, TX

Arrive Logistics

Data Scientist II

Austin, TX · On-site

Arrive Logistics is a leading transportation and technology company in North America, committed to ... annotation guidelines and ensuring label quality. • Evaluate and apply the appropriate approach ...

Arrive Logistics

Data Scientist II

Austin, TX · On-site

Who We Are Arrive Logistics is a leading transportation and technology company in North America ... Experience designing data annotation workflows, labeling guidelines, or label quality processes is ...

Arrive Logistics

Data Scientist II

Austin, TX · On-site +1

Who We Are Arrive Logistics is a leading transportation and technology company in North America ... Experience designing data annotation workflows, labeling guidelines, or label quality processes is ...

Welo Data

Data Labeling Associate

Austin, TX

$34/hr

WHAT YOU'LL DO • Execute Data labelling and annotation tasks across speech and voice datasets ... W2 Full-Time Employee • Hours: 40 hours per week • Work Authorization: Must be authorized to ...

TSMG

Robot Teleoperator (AI & Robotics)

Austin, TX · On-site

Support data annotation and quality validation activities * Maintain accurate operational records ... Work directly with cutting-edge robotics technology * Gain experience in one of the fastest-growing ...

TSMG

Robot Teleoperator (AI & Robotics)

Austin, TX

Support data annotation and quality validation activities * Maintain accurate operational records ... Work directly with cutting-edge robotics technology * Gain experience in one of the fastest-growing ...

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All JobsData Annotation Tech JobsFull Time Data Annotation Tech JobsFull Time Data Annotation Tech Jobs in Austin, TX

Full Time Data Annotation Tech information

See Austin, TX salary details

$12

$22

$34

How much do full time data annotation tech jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for full time data annotation tech in Austin, TX is $22.64, according to ZipRecruiter salary data. Most workers in this role earn between $16.68 and $26.92 per hour, depending on experience, location, and employer.

How much does a data annotation tech make per hour?

A full-time data annotation technician typically earns between $12 and $20 per hour, depending on experience, location, and the complexity of annotation tasks. Many roles require attention to detail and familiarity with annotation tools or platforms.

Can you do data annotation full time?

Full-time data annotation roles are available and typically involve working set hours, often 40 hours per week. These positions may require familiarity with annotation tools and attention to detail, and they often offer consistent schedules and remote or on-site options.

How hard is it to get hired by data annotation?

Getting hired as a full-time data annotation technician typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. The hiring process is generally straightforward, with many companies offering entry-level positions that do not require extensive experience or certifications.

How does a Full Time Data Annotation Tech typically collaborate with data scientists and engineers on projects?

As a Full Time Data Annotation Tech, you will regularly work alongside data scientists and engineers to ensure the accuracy and quality of labeled datasets used for machine learning models. Collaboration often involves attending project meetings to clarify annotation guidelines, providing feedback on ambiguous data cases, and updating annotation processes based on team input. Clear communication is essential, as your work directly impacts model performance and downstream analytics. This team-oriented environment fosters learning and provides insight into broader AI development workflows.

What are Full Time Data Annotation Techs?

Full Time Data Annotation Techs are professionals responsible for labeling and categorizing data used to train machine learning models. They examine various types of data, such as images, text, or audio, and apply specific tags or annotations according to project guidelines. Their work is essential in ensuring the accuracy of artificial intelligence systems by providing high-quality, structured datasets. Full-time positions typically involve working standard business hours and may require familiarity with specialized annotation tools and attention to detail.

What are the key skills and qualifications needed to thrive as a Full Time Data Annotation Tech, and why are they important?

To thrive as a Full Time Data Annotation Tech, you need strong attention to detail, basic data management skills, and familiarity with data labeling practices, typically supported by a high school diploma or equivalent. Experience with annotation tools (such as Labelbox, Supervisely, or similar platforms) and basic proficiency in spreadsheet or database systems are commonly required. Reliability, consistency, and effective communication are crucial soft skills for quality assurance and collaboration with data teams. These skills and qualities are essential to ensure the accuracy and efficiency of annotated datasets, which directly impact the performance of machine learning models.

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

AspectFull Time Data Annotation TechData Labeling Specialist
CredentialsBasic computer skills, attention to detailSimilar credentials, often with training in labeling tools
Work EnvironmentOffice or remote, collaborative teamsRemote or on-site, focused on labeling tasks
Industry UsageAI, machine learning, tech companiesAI, autonomous vehicles, healthcare
Job FocusAnnotating data for machine learning modelsLabeling data to improve AI accuracy

Both roles involve data annotation and labeling, often requiring similar skills and working environments. The main difference lies in job titles used by employers and the scope of responsibilities, with 'Full Time Data Annotation Tech' emphasizing a broader technical role, while 'Data Labeling Specialist' may focus more on specific labeling tasks.

Can you make a living off data annotation?

Full Time Data Annotation Tech roles can provide a stable income, especially with consistent work and experience. However, pay rates vary depending on the employer, location, and complexity of tasks, and many positions are part-time or freelance, which may affect earning potential.
What are the most commonly searched types of Data Annotation Tech jobs in Austin, TX? The most popular types of Data Annotation Tech jobs in Austin, TX are:
What are popular job titles related to Full Time Data Annotation Tech jobs in Austin, TX? For Full Time Data Annotation Tech jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Full Time Data Annotation Tech jobs in Austin, TX look for? The top searched job categories for Full Time Data Annotation Tech jobs in Austin, TX are:
Full Time Data Annotation Tech jobs near you
Data Scientist II

Data Scientist II

Arrive Logistics

Austin, TX • On-site

Full-time

Posted 23 days ago

Arrive Logistics rating

4.3

Company rating: 4.3 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description

Job Summary:
Arrive Logistics is a leading transportation and technology company in North America, committed to providing employees with a meaningful work experience. The Data Scientist II will work closely with Data Science, Product, and Engineering teams to build and improve ML and AI systems that drive operational value, focusing on text and language-based applications.
Responsibilities:
• Develop, evaluate, and iterate on NLP and LLM-based systems, including text classification, information extraction, and context retrieval pipelines.
• Build measurement and evaluation frameworks — both offline and online — to assess where and why systems are underperforming and quantify the impact of improvements.
• Develop golden test datasets and define methodologies for creating and maintaining them over time, including designing annotation guidelines and ensuring label quality.
• Evaluate and apply the appropriate approach for language tasks — whether prompt engineering, fine-tuning, or classical NLP methods — including modern retrieval and RAG architectures and LLM evaluation methodologies, based on the problem and available data.
• Perform structured analysis of system performance to surface failure modes, data gaps, and high-value areas for investment, applying sound statistical reasoning to evaluation results.
• Partner with engineers to support deployment, integration, and monitoring of ML and AI systems in production.
• Contribute to standards and best practices around deploying, evaluating, and monitoring text and language-based ML systems.
• Document work clearly and maintain knowledge artifacts that make systems understandable and maintainable over time.
• Collaborate with senior data scientists and cross-functional partners to translate business needs into well-scoped technical solutions, including communicating findings and recommendations to non-technical stakeholders.
Qualifications:
Required:
• Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics, or related) and 2–4 years of applied ML or data science experience, or equivalent practical experience.
• Hands-on experience building or improving NLP or LLM-based systems in applied settings.
• Familiarity with text classification, information extraction, or other NLP tasks — and an understanding of where these systems fail.
• Experience with both prompt engineering and fine-tuning approaches for language tasks, with the judgment to know when to apply each.
• Familiarity with modern retrieval strategies and RAG architectures and how they affect LLM system performance.
• Experience with Hugging Face Transformers for text classification or related NLP tasks.
• Experience contributing to evaluation frameworks, test sets, or performance diagnostics for ML systems, including comfort with statistical methods for measuring model performance.
• Proficiency in Python and SQL, and comfort working with structured and unstructured data.
• Ability to operate effectively in ambiguous problem spaces — scoping technical approaches when requirements are not fully defined.
• Strong written communication skills; able to document systems and findings clearly and present recommendations to non-technical stakeholders.
Preferred:
• Experience designing data annotation workflows, labeling guidelines, or label quality processes is a plus.
• Experience with model deployment, monitoring, or production ML workflows is a plus.
• Familiarity with LangChain and LangSmith or similar LLM orchestration and observability tooling is a plus.
• Transportation or logistics industry experience is a plus.
Company:
Arrive Logistics is a carrier and customer-centric logistics company that focuses on new standards for service in freight. Founded in 2014, the company is headquartered in Austin, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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