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Annotation Analyst Jobs in Austin, TX (NOW HIRING)

YO AI Labs

AI Finance Expert - Remote

Austin, TX · Remote

$100 - $200/hr

Financial Analysis * Technical & Report Writing * Business Communication * Content Review & Editing * Fact Checking * Data Interpretation * Data Annotation * Problem-Solving * Independent Research

PTP

Customer Experience Analyst - REMOTE

Austin, TX · Remote

Call Annotation: Perform detailed annotations to understand customer intents, evaluate AI comprehension, optimize self-service flows, and assess agent handling. Analysis and Data Optimization:

PTP

Customer Experience Analyst - REMOTE

Austin, TX · On-site

Call Annotation: Perform detailed annotations to understand customer intents, evaluate AI comprehension, optimize self-service flows, and assess agent handling. Analysis and Data Optimization:

PTP

Customer Experience Analyst - REMOTE

Austin, TX · On-site +1

Call Annotation: Perform detailed annotations to understand customer intents, evaluate AI comprehension, optimize self-service flows, and assess agent handling. Analysis and Data Optimization:

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All JobsAnnotation Analyst JobsAnnotation Analyst Jobs in Austin, TX

Annotation Analyst information

See Austin, TX salary details

$30.2K

$71.4K

$126.8K

How much do annotation analyst jobs pay per year?

As of Aug 30, 2026, the average yearly pay for annotation analyst in Austin, TX is $71,439.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,200.00 and $84,800.00 per year, depending on experience, location, and employer.

What is an annotation analyst?

An Annotation Analyst is responsible for labeling and categorizing data, such as text, images, audio, or video, to help improve machine learning models and AI systems. They follow specific guidelines to ensure accuracy and consistency in annotations. This role often involves reviewing, analyzing, and correcting data to enhance AI algorithms' performance. Annotation Analysts typically work with teams developing natural language processing (NLP), computer vision, or speech recognition technologies. Strong attention to detail and familiarity with industry tools are key skills for success in this role.

What are the typical day-to-day responsibilities of an annotation analyst?

As an Annotation Analyst, your daily responsibilities generally include reviewing and labeling large volumes of data—such as images, text, or audio—according to predefined guidelines or project requirements. You may also participate in quality control processes, address edge cases or ambiguities in the data, and collaborate with project managers, data scientists, or other analysts to ensure consistency and accuracy. Additionally, you might provide feedback on annotation tools or contribute to refining categorization criteria. The role often involves both independent work and teamwork within a fast-paced, detail-oriented environment, where precision is essential for supporting training and evaluation of AI or machine learning models.

What are the key skills and qualifications needed to thrive in the annotation analyst position, and why are they important?

To thrive as an Annotation Analyst, you need exceptional attention to detail, analytical thinking, and proficiency in data labeling or linguistic annotation, often supported by a relevant degree such as linguistics or computer science. Familiarity with annotation tools and platforms (like Labelbox, Prodigy, or proprietary systems) and knowledge of data privacy standards are highly beneficial. Strong communication, collaboration, and time management skills help in working efficiently with multidisciplinary teams and meeting project deadlines. These qualities are crucial for producing high-quality, accurate datasets that directly impact the success of machine learning and AI initiatives.

What are popular job titles related to Annotation Analyst jobs in Austin, TX?

For Annotation Analyst jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Annotation Analyst jobs in Austin, TX look for?

The top searched job categories for Annotation Analyst jobs in Austin, TX are:

Annotation Analyst jobs near you
Infographic showing various Annotation Analyst job openings in Austin, TX as of August 2026, with employment types broken down into 87% Full Time, 7% Part Time, and 6% Contract. Highlights an 82% Physical, 8% Hybrid, and 10% Remote job distribution, with an average salary of $72,600 per year, or $34.9 per hour.

Data Quality Analyst- Autonomous Vehicles

Austin, TX

Avride
See more jobs at Avrideexternal

Full-time

Posted 9 days ago

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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