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Full Time Data Annotation Tech Jobs in Austin, TX

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

As of Sep 5, 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.

What is a full time data annotation tech?

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?

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.

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 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.

Does full time data annotation tech actually pay?

Full-time data annotation technicians typically receive a regular salary or hourly wage, with pay rates varying based on experience, location, and company. Many roles offer benefits such as paid time off and health insurance, and some positions may require familiarity with annotation tools or specific data types.

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
Infographic showing various Full Time Data Annotation Tech job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $47,095 per year, or $22.6 per hour.

Data Quality Analyst- Autonomous Vehicles

Avride

Austin, TX • On-site

Full-time

Posted 15 days ago

Key responsibilities

  • Validate and analyze real-world data related to autonomous vehicle situations to ensure dataset quality and completeness.

  • Review annotation batches, monitor quality metrics, and investigate anomalies to maintain consistent data quality across workflows.

  • Use Python and database tools to automate data validation, process test results, and support operational and analytical tasks.

Job description

About the Role

Our autonomous vehicles encounter a wide variety of real-world situations on city streets. These situations need to be identified, categorized, and described in a structured way so they can be analyzed and used to evaluate the quality of our technology.

We are looking for a Data Quality Analyst who will help us validate and analyze real-world data related to the situations our autonomous vehicles encounter every day. In this role, you will work closely with production, develop quality metrics, and ensure consistent dataset quality across multiple annotation workflows.

You will be involved in the analysis and validation cycle: understanding test cases, working with collected data, validating its quality and completeness, analyzing results, identifying behavioral issues and coverage gaps, and determining where additional testing or investigation may be needed.

This is a data-focused role. You will work primarily with already collected data, test results, and driving scenarios, using analytical tools to understand vehicle behavior and support the testing and engineering teams.

This position is ideal for someone who is detail-oriented, comfortable working with complex datasets, and motivated to enhance data quality and annotation efficiency through analytical and technical approaches.

What You'll Do

Data Analytics

  • Analyze collected data across different road scenarios;
  • Analyze individual samples of data in detail to understand what happened and which factors may have affected situation;
  • Identify edge cases and scenarios that require additional investigation;
  • Monitor quality metrics on a regular basis and investigate anomalies;
  • Analyze dataset balance and completeness across locations, scenarios, and labels;
  • Assess how new scenes contribute not just more data, but better data.

Operational Routine & Controls

  • Regularly reviewing annotation batches (including repetitive checks) to make sure they meet quality and coverage expectations;
  • Maintain simple control dashboards and reports, update them on a weekly basis, and follow up on issues;
  • Take care of many small but important operational tasks that keep the annotation process stable and predictable.

Support & Automation

  • Use Python and ClickHouse for analysis, monitoring, and process support;
  • Work closely with product and engineering teams to implement improvements;
  • Automate data validation, test-result processing, coverage analysis, and repetitive investigation workflows.
What You'll Need
  • A degree in a relevant field (Computer Science, Quality assurance, Data Analytics, Engineering, or another technical discipline);
  • Strong analytical thinking and attention to detail - you'll frequently review data, spot anomalies, and work through repetitive validation tasks;
  • Practical Python skills for data processing and analysis (Pandas);
  • Ability to query databases and work with analytical data stacks;
  • Readiness to handle routine and sometimes monotonous work - dataset checks, manual validations, and weekly quality reviews are a core part of the role;
  • A process-oriented mindset: ability to follow existing workflows, maintain consistency, and keep documentation and reports up to date;
  • A valid driver's license and practical driving experience.
Nice to Have
  • Experience with ClickHouse
  • Experience with QA, testing, or validation workflows;
  • Strong systems thinking, attention to quality, and a proactive mindset.
  • Experience using semi-automated labeling tools or active learning methods.

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